The lnRR_func function is here used to calculate a log response ratio (lnRR) adjusted for small sample sizes. In addition, this formula accounts for correlated samples. For more details, see Doncaster and Spake (2018) Correction for bias in meta-analysis of little-replicated studies. Methods in Ecology and Evolution; 9:634-644
# packages
library(tidyverse)
library(googlesheets4)
library(here)
library(metafor)
library(metaAidR) # see a note above
library(orchaRd) # see a note above
library(ape)
library(clubSandwich)
library(metaAidR)
library(patchwork)
library(emmeans) # see a note above
library(kableExtra)
library(GGally)
library(cowplot)
library(grDevices) # reqired for using base and ggplots together
# Below is the custom function to calculate the lnRR
lnRR_func <- function(Mc, Nc, Me, Ne, aCV2c, aCV2e, rho = 0.5){
lnRR <- log(Me/Mc) +
0.5 * ((aCV2e/Ne) - (aCV2c/Nc))
var_lnRR <- (aCV2c/Nc) + (aCV2e/Ne) -
2 * rho * ((aCV2c*aCV2e)/sqrt(Nc*Ne))
data.frame(lnRR,var_lnRR)
}
# Mc: Concentration of PFAS of the raw (control) sample
# Nc: Sample size of the raw (control) sample
# Me: Concentration of PFAS of the cooked (experimental) sample
# Ne: Sample size of the cooked (experimental) sample
# aCV2c: Mean coefficient of variation of the raw (control) samples
# aCV2e: Mean coefficient of variation of the cooked (experimental) samplesraw_data <- read_sheet("https://docs.google.com/spreadsheets/d/1cbmYDfIc2dxHJxBaowojUZZkN31NW4sL_pHw0t9eTTU/edit#gid=477880397", range = "Data_extraction_2", skip=1, col_types = "ccncccccncncccccnncccnccnncncnccnnncncncccccccc") # Import raw dataprocessed_data <- filter(raw_data, !PFAS_type == 'PFOS_Total')
processed_data <- filter(processed_data, !Species_common == 'Fish cake')
write.csv(processed_data, here("data", "pilot_data_preprocessed.csv"), row.names = F)processed_data <- read.csv(here("data", "pilot_data_preprocessed.csv"))
dat <- processed_data %>% mutate(SDc = ifelse(Sc_technical_biological == "biological", Sc, NA), # Calculate the SD of biological replicates for control samples
SDe = ifelse(Se_technical_biological == "biological", Se, NA)) # Calculate the SD of biological replicates for experimental samples
kable(dat, "html") %>% kable_styling("striped", position = "left") %>% scroll_box(width = "100%", height = "500px")| Study_ID | Author_year | Publication_year | Country_firstAuthor | Effect_ID | Species_common | Species_Scientific | Invertebrate_vertebrate | Fish_mollusc | Moisture_loss_in_percent | PFAS_type | PFAS_carbon_chain | linear_total | Choice_of_9 | Cooking_method | Cooking_Category | Comments_cooking | Temperature_in_Celsius | Length_cooking_time_in_s | Water | Oil | Oil_type | Volume_liquid_ml | Volume_liquid_ml_0 | Ratio_liquid_fish | Weigh_g_sample | Cohort_ID | Cohort_comment | Nc | Pooled_Nc | Unit_PFAS_conc | Mc | Mc_comment | Sc | sd | Sc_technical_biological | Ne | Pooled_Ne | Me | Me_comment | Se | Se_technical_biological | If_technical_how_many | Unit_LOD_LOQ | LOD | LOQ | Design | DataSource | Raw_data_provided | General_comments | checked | SDc | SDe |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| F001 | Alves_2017 | 2017 | Portugal | E001 | Flounder | Platichthys flesus | vertebrate | marine fish | 7.430000 | PFOS | 8 | linear | Yes | Steaming | water-based | NA | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C001 | NA | 25 | 1 | ng/g | 24.0000000 | NA | 1.5280000 | sd | technical | 25 | 1 | 22.0000000 | NA | 1.5300000 | technical | 2 | ng/g | <0.1 | <0.2 | Dependent | Table 3 | No | Authors replied | ML - ok | NA | NA |
| F001 | Alves_2017 | 2017 | Portugal | E002 | Mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFUnDA | 11 | NA | Yes | Steaming | water-based | NA | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C002 | NA | 25 | 1 | ng/g | 3.1000000 | NA | 0.2120000 | sd | technical | 25 | 1 | 2.9000000 | NA | 0.1410000 | technical | 2 | ng/g | <0.1 | <0.2 | Dependent | Table 3 | No | Authors replied | ML - ok | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E003 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.860000 | PFUnDA | 11 | NA | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | NA | 25 | 1 | ng/g | 13.3018868 | NA | 0.0471698 | sd | technical | 25 | 1 | 4.1509434 | NA | 0.0943396 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E004 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.860000 | PFDoDA | 12 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | NA | 25 | 1 | ng/g | 3.5731707 | NA | 0.0243902 | sd | technical | 25 | 1 | 3.2073171 | NA | 0.0243902 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E005 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.860000 | PFTrA | 13 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | NA | 25 | 1 | ng/g | 6.5283019 | NA | 0.0754717 | sd | technical | 25 | 1 | 10.0377358 | NA | 0.0754717 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E006 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.860000 | PFTA | 14 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | NA | 25 | 1 | ng/g | 1.3736842 | NA | 0.0157895 | sd | technical | 25 | 1 | 1.3315789 | NA | 0.0210526 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E007 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.860000 | PFOS | 8 | total | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | NA | 25 | 1 | ng/g | 0.6467391 | NA | 0.0054348 | sd | technical | 25 | 1 | 0.3016304 | NA | 0.0081522 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E008 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.860000 | PFDA | 10 | NA | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | NA | 25 | 1 | ng/g | 0.0250000 | <LOQ | NA | sd | technical | 25 | 1 | 0.0869767 | NA | 0.0130233 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E009 | European plaice | Pleuronectes platessa | vertebrate | marine fish | 8.700000 | PFOS | 8 | total | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C004 | NA | 25 | 1 | ng/g | 0.2472826 | NA | 0.0081522 | sd | technical | 25 | 1 | 0.2527174 | NA | 0.0054348 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E010 | blue mussel | Mytilus edulis | invertebrate | mollusca | 6.770000 | PFBA | 3 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C005 | NA | 50 | 1 | ng/g | 0.0250000 | <LOQ | NA | sd | technical | 50 | 1 | 0.2083333 | NA | 0.0090909 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E011 | blue mussel | Mytilus edulis | invertebrate | mollusca | 6.770000 | PFDA | 10 | NA | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C005 | NA | 50 | 1 | ng/g | 0.0241860 | NA | 0.0074419 | sd | technical | 50 | 1 | 0.0250000 | <LOQ | NA | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA |
| F003 | Bhavsar_2014 | 2014 | Canada | E012 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFNA | 9 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.0670000 | NA | 0.0950000 | sd | biological | 5 | 5 | 0.0860000 | NA | 0.1350000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | ML | 0.0950000 | 0.1350000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E013 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFDA | 10 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.1560000 | NA | 0.1970000 | sd | biological | 5 | 5 | 0.1920000 | NA | 0.2660000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1970000 | 0.2660000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E014 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFUnDA | 11 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.1860000 | NA | 0.2250000 | sd | biological | 5 | 5 | 0.2340000 | NA | 0.2910000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2250000 | 0.2910000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E015 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFDoDA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.0800000 | NA | 0.0730000 | sd | biological | 5 | 5 | 0.1010000 | NA | 0.0950000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0730000 | 0.0950000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E016 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFTrA | 13 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.2150000 | NA | 0.1830000 | sd | biological | 5 | 5 | 0.2590000 | NA | 0.2410000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1830000 | 0.2410000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E017 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFTA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.0760000 | NA | 0.0550000 | sd | biological | 5 | 5 | 0.0830000 | NA | 0.0730000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0550000 | 0.0730000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E019 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFOS | 8 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 12.7000000 | NA | 12.6100000 | sd | biological | 5 | 5 | 16.5600000 | NA | 18.0000000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 12.6100000 | 18.0000000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E020 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | PFDS | 10 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.3030000 | NA | 0.2840000 | sd | biological | 5 | 5 | 0.3970000 | NA | 0.4330000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2840000 | 0.4330000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E021 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | 6:6PFPIA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.0030000 | NA | 0.0030000 | sd | biological | 5 | 5 | 0.0020000 | NA | 0.0020000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0030000 | 0.0020000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E022 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.400000 | 6:8PFPIA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | NA | 5 | 5 | ng/g | 0.0170000 | NA | 0.0230000 | sd | biological | 5 | 5 | 0.0100000 | NA | 0.0160000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0230000 | 0.0160000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E023 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFNA | 9 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.0670000 | NA | 0.0950000 | sd | biological | 5 | 5 | 0.0830000 | NA | 0.1180000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0950000 | 0.1180000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E024 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFDA | 10 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.1560000 | NA | 0.1970000 | sd | biological | 5 | 5 | 0.1900000 | NA | 0.2320000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1970000 | 0.2320000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E025 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFUnDA | 11 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.1860000 | NA | 0.2250000 | sd | biological | 5 | 5 | 0.2560000 | NA | 0.3100000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2250000 | 0.3100000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E026 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFDoDA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.0800000 | NA | 0.0730000 | sd | biological | 5 | 5 | 0.1000000 | NA | 0.0800000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0730000 | 0.0800000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E027 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFTrA | 13 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.2150000 | NA | 0.1830000 | sd | biological | 5 | 5 | 0.2850000 | NA | 0.2340000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1830000 | 0.2340000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E028 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFTA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.0760000 | NA | 0.0550000 | sd | biological | 5 | 5 | 0.0830000 | NA | 0.0710000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0550000 | 0.0710000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E030 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFOS | 8 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 12.7000000 | NA | 12.6100000 | sd | biological | 5 | 5 | 16.4500000 | NA | 15.6300000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 12.6100000 | 15.6300000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E031 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | PFDS | 10 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.3030000 | NA | 0.2840000 | sd | biological | 5 | 5 | 0.3920000 | NA | 0.3590000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2840000 | 0.3590000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E032 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | 6:6PFPIA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.0030000 | NA | 0.0030000 | sd | biological | 5 | 5 | 0.0020000 | NA | 0.0030000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0030000 | 0.0030000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E033 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.680000 | 6:8PFPIA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | NA | 5 | 5 | ng/g | 0.0170000 | NA | 0.0230000 | sd | biological | 5 | 5 | 0.0140000 | NA | 0.0220000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0230000 | 0.0220000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E034 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFNA | 9 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.0670000 | NA | 0.0950000 | sd | biological | 5 | 5 | 0.0780000 | NA | 0.1140000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0950000 | 0.1140000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E035 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.1560000 | NA | 0.1970000 | sd | biological | 5 | 5 | 0.1820000 | NA | 0.2220000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1970000 | 0.2220000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E036 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.1860000 | NA | 0.2250000 | sd | biological | 5 | 5 | 0.2270000 | NA | 0.2550000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2250000 | 0.2550000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E037 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.0800000 | NA | 0.0730000 | sd | biological | 5 | 5 | 0.0960000 | NA | 0.0810000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0730000 | 0.0810000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E038 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFTrA | 13 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.2150000 | NA | 0.1830000 | sd | biological | 5 | 5 | 0.2750000 | NA | 0.2160000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1830000 | 0.2160000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E039 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFTA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.0760000 | NA | 0.0550000 | sd | biological | 5 | 5 | 0.0870000 | NA | 0.0670000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0550000 | 0.0670000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E041 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFOS | 8 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 12.7000000 | NA | 12.6100000 | sd | biological | 5 | 5 | 16.0300000 | NA | 15.1900000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 12.6100000 | 15.1900000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E042 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | PFDS | 10 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.3030000 | NA | 0.2840000 | sd | biological | 5 | 5 | 0.3930000 | NA | 0.3690000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2840000 | 0.3690000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E043 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | 6:6PFPIA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.0030000 | NA | 0.0030000 | sd | biological | 5 | 5 | 0.0020000 | NA | 0.0030000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0030000 | 0.0030000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E044 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.680000 | 6:8PFPIA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | NA | 5 | 5 | ng/g | 0.0170000 | NA | 0.0230000 | sd | biological | 5 | 5 | 0.0130000 | NA | 0.0220000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0230000 | 0.0220000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E045 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFNA | 9 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.0920000 | NA | 0.0300000 | sd | biological | 5 | 5 | 0.0990000 | NA | 0.0220000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0300000 | 0.0220000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E046 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFDA | 10 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.5180000 | NA | 0.1070000 | sd | biological | 5 | 5 | 0.5660000 | NA | 0.1380000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1070000 | 0.1380000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E047 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFUnDA | 11 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.7120000 | NA | 0.1580000 | sd | biological | 5 | 5 | 0.8040000 | NA | 0.1670000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1580000 | 0.1670000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E048 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFDoDA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.9890000 | NA | 0.3170000 | sd | biological | 5 | 5 | 1.0960000 | NA | 0.3960000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.3170000 | 0.3960000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E049 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFTrA | 13 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.7790000 | NA | 0.4400000 | sd | biological | 5 | 5 | 0.7740000 | NA | 0.3320000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.4400000 | 0.3320000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E050 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFTA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.9510000 | NA | 0.6470000 | sd | biological | 5 | 5 | 1.1400000 | NA | 0.8740000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.6470000 | 0.8740000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E051 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFHxS | 6 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.2920000 | NA | 0.3190000 | sd | biological | 5 | 5 | 0.3410000 | NA | 0.3910000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.3190000 | 0.3910000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E052 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFOS | 8 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 27.1700000 | NA | 7.7680000 | sd | biological | 5 | 5 | 30.5200000 | NA | 9.2540000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 7.7680000 | 9.2540000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E053 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | PFDS | 10 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.9110000 | NA | 0.5320000 | sd | biological | 5 | 5 | 1.0840000 | NA | 0.5710000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.5320000 | 0.5710000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E054 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | 6:6PFPIA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | NA | 5 | 5 | ng/g | 0.0980000 | NA | 0.0600000 | sd | biological | 5 | 5 | 0.1050000 | NA | 0.0600000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0600000 | 0.0600000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E055 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.470000 | 6:8PFPIA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C010 | NA | 5 | 5 | ng/g | 0.1670000 | NA | 0.0770000 | sd | biological | 5 | 5 | 0.1800000 | NA | 0.0840000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0770000 | 0.0840000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E056 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFNA | 9 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.0920000 | NA | 0.0300000 | sd | biological | 5 | 5 | 0.1050000 | NA | 0.0370000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0300000 | 0.0370000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E057 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFDA | 10 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.5180000 | NA | 0.1070000 | sd | biological | 5 | 5 | 0.5480000 | NA | 0.1210000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1070000 | 0.1210000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E058 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFUnDA | 11 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.7120000 | NA | 0.1580000 | sd | biological | 5 | 5 | 0.8480000 | NA | 0.1550000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1580000 | 0.1550000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E059 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFDoDA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.9890000 | NA | 0.3170000 | sd | biological | 5 | 5 | 1.1080000 | NA | 0.4040000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.3170000 | 0.4040000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E060 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFTrA | 13 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.7790000 | NA | 0.4400000 | sd | biological | 5 | 5 | 0.8280000 | NA | 0.4180000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.4400000 | 0.4180000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E061 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFTA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.9510000 | NA | 0.6470000 | sd | biological | 5 | 5 | 1.1150000 | NA | 0.7690000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.6470000 | 0.7690000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E062 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFHxS | 6 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.2920000 | NA | 0.3190000 | sd | biological | 5 | 5 | 0.2910000 | NA | 0.3460000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.3190000 | 0.3460000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E063 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFOS | 8 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 27.1700000 | NA | 7.7680000 | sd | biological | 5 | 5 | 28.3700000 | NA | 11.9900000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 7.7680000 | 11.9900000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E064 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | PFDS | 10 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.9110000 | NA | 0.5320000 | sd | biological | 5 | 5 | 1.0450000 | NA | 0.6230000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.5320000 | 0.6230000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E065 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | 6:6PFPIA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.0980000 | NA | 0.0600000 | sd | biological | 5 | 5 | 0.1170000 | NA | 0.0730000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0600000 | 0.0730000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E066 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.680000 | 6:8PFPIA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | NA | 5 | 5 | ng/g | 0.1670000 | NA | 0.0770000 | sd | biological | 5 | 5 | 0.1900000 | NA | 0.0800000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0770000 | 0.0800000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E067 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFNA | 9 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.0920000 | NA | 0.0300000 | sd | biological | 5 | 5 | 0.1010000 | NA | 0.0350000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0300000 | 0.0350000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E068 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.5180000 | NA | 0.1070000 | sd | biological | 5 | 5 | 0.5690000 | NA | 0.1080000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1070000 | 0.1080000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E069 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.7120000 | NA | 0.1580000 | sd | biological | 5 | 5 | 0.8300000 | NA | 0.1300000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1580000 | 0.1300000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E070 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.9890000 | NA | 0.3170000 | sd | biological | 5 | 5 | 1.0440000 | NA | 0.3560000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.3170000 | 0.3560000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E071 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFTrA | 13 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.7790000 | NA | 0.4400000 | sd | biological | 5 | 5 | 0.7460000 | NA | 0.2830000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.4400000 | 0.2830000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E072 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFTA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.9510000 | NA | 0.6470000 | sd | biological | 5 | 5 | 1.0670000 | NA | 0.7540000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.6470000 | 0.7540000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E073 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFHxS | 6 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.2920000 | NA | 0.3190000 | sd | biological | 5 | 5 | 0.3590000 | NA | 0.4280000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.3190000 | 0.4280000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E074 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFOS | 8 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 27.1700000 | NA | 7.7680000 | sd | biological | 5 | 5 | 28.1100000 | NA | 10.9300000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 7.7680000 | 10.9300000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E075 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | PFDS | 10 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.9110000 | NA | 0.5320000 | sd | biological | 5 | 5 | 1.0900000 | NA | 0.6180000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.5320000 | 0.6180000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E076 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | 6:6PFPIA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.0980000 | NA | 0.0600000 | sd | biological | 5 | 5 | 0.1060000 | NA | 0.0650000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0600000 | 0.0650000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E077 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.910000 | 6:8PFPIA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | NA | 5 | 5 | ng/g | 0.1670000 | NA | 0.0770000 | sd | biological | 5 | 5 | 0.1880000 | NA | 0.0750000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0770000 | 0.0750000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E078 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFNA | 9 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.2980000 | NA | 0.1430000 | sd | biological | 4 | 4 | 0.3700000 | NA | 0.1890000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1430000 | 0.1890000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E079 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFDA | 10 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.4230000 | NA | 0.1860000 | sd | biological | 4 | 4 | 0.5100000 | NA | 0.2320000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1860000 | 0.2320000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E080 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFUnDA | 11 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.5600000 | NA | 0.2510000 | sd | biological | 4 | 4 | 0.6850000 | NA | 0.2930000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2510000 | 0.2930000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E081 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFDoDA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.1980000 | NA | 0.0950000 | sd | biological | 4 | 4 | 0.2210000 | NA | 0.1140000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0950000 | 0.1140000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E082 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFTrA | 13 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.4610000 | NA | 0.2170000 | sd | biological | 4 | 4 | 0.4840000 | NA | 0.2640000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2170000 | 0.2640000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E083 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFTA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.1280000 | NA | 0.0510000 | sd | biological | 4 | 4 | 0.1370000 | NA | 0.0510000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0510000 | 0.0510000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E084 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFHxS | 6 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.2580000 | NA | 0.0550000 | sd | biological | 4 | 4 | 0.2480000 | NA | 0.0610000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0550000 | 0.0610000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E085 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFOS | 8 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 18.1800000 | NA | 6.6860000 | sd | biological | 4 | 4 | 20.5100000 | NA | 6.7520000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 6.6860000 | 6.7520000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E086 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | PFDS | 10 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.4560000 | NA | 0.1770000 | sd | biological | 4 | 4 | 0.4740000 | NA | 0.1960000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1770000 | 0.1960000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E087 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | 6:6PFPIA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.0020000 | NA | 0.0010000 | sd | biological | 4 | 4 | 0.0020000 | NA | 0.0020000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0010000 | 0.0020000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E088 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.130000 | 6:8PFPIA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | NA | 4 | 4 | ng/g | 0.0170000 | NA | 0.0090000 | sd | biological | 4 | 4 | 0.0180000 | NA | 0.0090000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0090000 | 0.0090000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E089 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFNA | 9 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.2980000 | NA | 0.1430000 | sd | biological | 4 | 4 | 0.3580000 | NA | 0.1700000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1430000 | 0.1700000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E090 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFDA | 10 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.4230000 | NA | 0.1860000 | sd | biological | 4 | 4 | 0.5280000 | NA | 0.2330000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1860000 | 0.2330000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E091 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFUnDA | 11 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.5600000 | NA | 0.2510000 | sd | biological | 4 | 4 | 0.7250000 | NA | 0.3450000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2510000 | 0.3450000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E092 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFDoDA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.1980000 | NA | 0.0950000 | sd | biological | 4 | 4 | 0.2370000 | NA | 0.1110000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0950000 | 0.1110000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E093 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFTrA | 13 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.4610000 | NA | 0.2170000 | sd | biological | 4 | 4 | 0.5580000 | NA | 0.2800000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2170000 | 0.2800000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E094 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFTA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.1280000 | NA | 0.0510000 | sd | biological | 4 | 4 | 0.1490000 | NA | 0.0680000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0510000 | 0.0680000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E095 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFHxS | 6 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.2580000 | NA | 0.0550000 | sd | biological | 4 | 4 | 0.2630000 | NA | 0.0870000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0550000 | 0.0870000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E096 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFOS | 8 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 18.1800000 | NA | 6.6860000 | sd | biological | 4 | 4 | 22.1100000 | NA | 7.8970000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 6.6860000 | 7.8970000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E097 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | PFDS | 10 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.4560000 | NA | 0.1770000 | sd | biological | 4 | 4 | 0.5600000 | NA | 0.2260000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1770000 | 0.2260000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E098 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | 6:6PFPIA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.0020000 | NA | 0.0010000 | sd | biological | 4 | 4 | 0.0120000 | NA | 0.0180000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0010000 | 0.0180000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E099 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.230000 | 6:8PFPIA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | NA | 4 | 4 | ng/g | 0.0170000 | NA | 0.0090000 | sd | biological | 4 | 4 | 0.0160000 | NA | 0.0060000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0090000 | 0.0060000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E100 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFNA | 9 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.2980000 | NA | 0.1430000 | sd | biological | 4 | 4 | 0.3740000 | NA | 0.1810000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1430000 | 0.1810000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E101 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.4230000 | NA | 0.1860000 | sd | biological | 4 | 4 | 0.4930000 | NA | 0.2070000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1860000 | 0.2070000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E102 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.5600000 | NA | 0.2510000 | sd | biological | 4 | 4 | 0.6830000 | NA | 0.2860000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2510000 | 0.2860000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E103 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.1980000 | NA | 0.0950000 | sd | biological | 4 | 4 | 0.2320000 | NA | 0.1030000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0950000 | 0.1030000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E104 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFTrA | 13 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.4610000 | NA | 0.2170000 | sd | biological | 4 | 4 | 0.5190000 | NA | 0.2120000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.2170000 | 0.2120000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E105 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFTA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.1280000 | NA | 0.0510000 | sd | biological | 4 | 4 | 0.1290000 | NA | 0.0450000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0510000 | 0.0450000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E106 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFHxS | 6 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.2580000 | NA | 0.0550000 | sd | biological | 4 | 4 | 0.2450000 | NA | 0.0770000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0550000 | 0.0770000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E107 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFOS | 8 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 18.1800000 | NA | 6.6860000 | sd | biological | 4 | 4 | 21.6700000 | NA | 8.0080000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 6.6860000 | 8.0080000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E108 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | PFDS | 10 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.4560000 | NA | 0.1770000 | sd | biological | 4 | 4 | 0.5160000 | NA | 0.2440000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.1770000 | 0.2440000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E109 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | 6:6PFPIA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.0020000 | NA | 0.0010000 | sd | biological | 4 | 4 | 0.0020000 | NA | 0.0010000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0010000 | 0.0010000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E110 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.530000 | 6:8PFPIA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | NA | 4 | 4 | ng/g | 0.0170000 | NA | 0.0090000 | sd | biological | 4 | 4 | 0.0160000 | NA | 0.0060000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0090000 | 0.0060000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E111 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFNA | 9 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.0630000 | NA | 0.0210000 | sd | biological | 5 | 5 | 0.0790000 | NA | 0.0230000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0210000 | 0.0230000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E112 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFDA | 10 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.2480000 | NA | 0.0400000 | sd | biological | 5 | 5 | 0.3490000 | NA | 0.0940000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0400000 | 0.0940000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E113 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFUnDA | 11 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.2390000 | NA | 0.0400000 | sd | biological | 5 | 5 | 0.3330000 | NA | 0.0910000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0400000 | 0.0910000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E114 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFDoDA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.1050000 | NA | 0.0190000 | sd | biological | 5 | 5 | 0.1330000 | NA | 0.0120000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0190000 | 0.0120000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E115 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFTrA | 13 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.1490000 | NA | 0.0200000 | sd | biological | 5 | 5 | 0.1800000 | NA | 0.0210000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0200000 | 0.0210000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E116 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFTA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.0690000 | NA | 0.0090000 | sd | biological | 5 | 5 | 0.0930000 | NA | 0.0230000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0090000 | 0.0230000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E117 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFHxS | 6 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.0800000 | NA | 0.0250000 | sd | biological | 5 | 5 | 0.0980000 | NA | 0.0340000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0250000 | 0.0340000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E118 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFOS | 8 | NA | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 36.7900000 | NA | 1.6240000 | sd | biological | 5 | 5 | 45.0900000 | NA | 3.7090000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 1.6240000 | 3.7090000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E119 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | PFDS | 10 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.1060000 | NA | 0.0240000 | sd | biological | 5 | 5 | 0.1780000 | NA | 0.0940000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0240000 | 0.0940000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E120 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | 6:6PFPIA | 12 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.0260000 | NA | 0.0060000 | sd | biological | 5 | 5 | 0.0350000 | NA | 0.0060000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0060000 | 0.0060000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E121 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.710000 | 6:8PFPIA | 14 | NA | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | NA | 5 | 5 | ng/g | 0.0670000 | NA | 0.0100000 | sd | biological | 5 | 5 | 0.0630000 | NA | 0.0170000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0100000 | 0.0170000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E122 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFNA | 9 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.0630000 | NA | 0.0210000 | sd | biological | 5 | 5 | 0.0740000 | NA | 0.0140000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0210000 | 0.0140000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E123 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFDA | 10 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.2480000 | NA | 0.0400000 | sd | biological | 5 | 5 | 0.3380000 | NA | 0.0980000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0400000 | 0.0980000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E124 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFUnDA | 11 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.2390000 | NA | 0.0400000 | sd | biological | 5 | 5 | 0.3480000 | NA | 0.1020000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0400000 | 0.1020000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E125 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFDoDA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.1050000 | NA | 0.0190000 | sd | biological | 5 | 5 | 0.1440000 | NA | 0.0370000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0190000 | 0.0370000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E126 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFTrA | 13 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.1490000 | NA | 0.0200000 | sd | biological | 5 | 5 | 0.2170000 | NA | 0.0410000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0200000 | 0.0410000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E127 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFTA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.0690000 | NA | 0.0090000 | sd | biological | 5 | 5 | 0.0940000 | NA | 0.0250000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0090000 | 0.0250000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E128 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFHxS | 6 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.0800000 | NA | 0.0250000 | sd | biological | 5 | 5 | 0.0880000 | NA | 0.0360000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0250000 | 0.0360000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E129 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFOS | 8 | NA | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 36.7900000 | NA | 1.6240000 | sd | biological | 5 | 5 | 52.6900000 | NA | 14.6200000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 1.6240000 | 14.6200000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E130 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | PFDS | 10 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.1060000 | NA | 0.0240000 | sd | biological | 5 | 5 | 0.1890000 | NA | 0.0800000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0240000 | 0.0800000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E131 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | 6:6PFPIA | 12 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.0260000 | NA | 0.0060000 | sd | biological | 5 | 5 | 0.0400000 | NA | 0.0080000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0060000 | 0.0080000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E132 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.090000 | 6:8PFPIA | 14 | NA | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | NA | 5 | 5 | ng/g | 0.0670000 | NA | 0.0100000 | sd | biological | 5 | 5 | 0.0870000 | NA | 0.0120000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0100000 | 0.0120000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E133 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFNA | 9 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.0630000 | NA | 0.0210000 | sd | biological | 5 | 5 | 0.0670000 | NA | 0.0150000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0210000 | 0.0150000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E134 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.2480000 | NA | 0.0400000 | sd | biological | 5 | 5 | 0.2990000 | NA | 0.0720000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0400000 | 0.0720000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E135 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.2390000 | NA | 0.0400000 | sd | biological | 5 | 5 | 0.3070000 | NA | 0.0760000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0400000 | 0.0760000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E136 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.1050000 | NA | 0.0190000 | sd | biological | 5 | 5 | 0.1290000 | NA | 0.0490000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0190000 | 0.0490000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E137 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFTrA | 13 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.1490000 | NA | 0.0200000 | sd | biological | 5 | 5 | 0.1790000 | NA | 0.0540000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0200000 | 0.0540000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E138 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFTA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.0690000 | NA | 0.0090000 | sd | biological | 5 | 5 | 0.0870000 | NA | 0.0340000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0090000 | 0.0340000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E139 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFHxS | 6 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.0800000 | NA | 0.0250000 | sd | biological | 5 | 5 | 0.0830000 | NA | 0.0270000 | biological | NA | ng/g | Probably <0.006 | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0250000 | 0.0270000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E140 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFOS | 8 | NA | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 36.7900000 | NA | 1.6240000 | sd | biological | 5 | 5 | 44.5100000 | NA | 7.7180000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 1.6240000 | 7.7180000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E141 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | PFDS | 10 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.1060000 | NA | 0.0240000 | sd | biological | 5 | 5 | 0.1570000 | NA | 0.0660000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0240000 | 0.0660000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E142 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | 6:6PFPIA | 12 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.0260000 | NA | 0.0060000 | sd | biological | 5 | 5 | 0.0290000 | NA | 0.0040000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0060000 | 0.0040000 |
| F003 | Bhavsar_2014 | 2014 | Canada | E143 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.450000 | 6:8PFPIA | 14 | NA | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | NA | 5 | 5 | ng/g | 0.0670000 | NA | 0.0100000 | sd | biological | 5 | 5 | 0.0770000 | NA | 0.0050000 | biological | NA | ng/g | Not provided | Not provided | Shared control | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | NA | 0.0100000 | 0.0050000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E144 | Catfish | Ictalurus punctatus | vertebrate | freshwater fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C017 | NA | 19 | 1 | ng/g | 1.5657252 | NA | NA | Not available because sample size is one. | technical | 19 | 1 | 0.8987374 | NA | NA | technical | 4 | ng/g | 0.3646058391 | 1.093817517 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | ML | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E145 | Grouper | Epinephelus itajara | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C018 | NA | 14 | 1 | ng/g | 1.3600000 | NA | NA | Not available because sample size is one. | technical | 14 | 1 | 0.0169896 | LOD | NA | technical | 4 | ng/g | 0.01698962618 | 0.05096887855 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E146 | Grouper | Epinephelus itajara | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C018 | NA | 14 | 1 | ng/g | 0.3715856 | LOD | NA | Not available because sample size is one. | technical | 14 | 1 | 0.4700000 | NA | NA | technical | 4 | ng/g | 0.3715856481 | 1.114756944 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E147 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | NA | 9 | 1 | ng/g | 0.0774969 | LOD | NA | Not available because sample size is one. | technical | 9 | 1 | 0.0600000 | NA | NA | technical | 4 | ng/g | 0.07749693852 | 0.2324908155 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E148 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | NA | 9 | 1 | ng/g | 1.3400000 | NA | NA | Not available because sample size is one. | technical | 9 | 1 | 0.0032120 | LOD | NA | technical | 4 | ng/g | 0.0032120281 | 0.009636084301 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E149 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | NA | 9 | 1 | ng/g | 0.0270203 | LOD | NA | Not available because sample size is one. | technical | 9 | 1 | 0.3900000 | NA | NA | technical | 4 | ng/g | 0.02702032357 | 0.08106097072 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E150 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | NA | 9 | 1 | ng/g | 1.3400000 | NA | NA | Not available because sample size is one. | technical | 9 | 1 | 0.2200000 | NA | NA | technical | 4 | ng/g | 0.2333732266 | 0.7001196799 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E151 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | NA | 15 | 1 | ng/g | 0.7800000 | NA | NA | Not available because sample size is one. | technical | 15 | 1 | 0.0600000 | NA | NA | technical | 3 | ng/g | 0.02612585327 | 0.0783775598 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E152 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | NA | 15 | 1 | ng/g | 1.2900000 | NA | NA | Not available because sample size is one. | technical | 15 | 1 | 0.0261259 | LOD | NA | technical | 3 | ng/g | 0.02612585327 | 0.0783775598 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E153 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFDA | 10 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | NA | 15 | 1 | ng/g | 1.5500000 | NA | NA | Not available because sample size is one. | technical | 15 | 1 | 0.0120876 | LOD | NA | technical | 3 | ng/g | 0.01208759187 | 0.03626277562 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E154 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | NA | 15 | 1 | ng/g | 1.8800000 | NA | NA | Not available because sample size is one. | technical | 15 | 1 | 1.5900000 | NA | NA | technical | 3 | ng/g | 0.02340346342 | 0.07021039026 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E155 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFTA | 14 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | NA | 15 | 1 | ng/g | 2.6100000 | NA | NA | Not available because sample size is one. | technical | 15 | 1 | 0.0071943 | LOD | NA | technical | 3 | ng/g | 0.007194278092 | 0.02158283428 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E156 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | NA | 15 | 1 | ng/g | 0.5086163 | LOD | NA | Not available because sample size is one. | technical | 15 | 1 | 0.2300000 | NA | NA | technical | 3 | ng/g | 0.5086163051 | 1.525848915 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E157 | Red snapper | Lutjanus campechanus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C021 | NA | 19 | 1 | ng/g | 1.4600000 | NA | NA | Not available because sample size is one. | technical | 19 | 1 | 0.2100000 | NA | NA | technical | 4 | ng/g | 0.335745729 | 1.007237187 | Shared control | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E158 | Red snapper | Lutjanus campechanus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C021 | NA | 19 | 1 | ng/g | 1.4600000 | NA | NA | Not available because sample size is one. | technical | 19 | 1 | 0.7800000 | NA | NA | technical | 4 | ng/g | 0.2127077334 | 0.6381232001 | Shared control | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E159 | Sea squirt | Diplosoma listerianum | vertebrate | tunicata | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C022 | NA | 22 | 1 | ng/g | 1.5800000 | NA | NA | Not available because sample size is one. | technical | 22 | 1 | 1.5900000 | NA | NA | technical | 3 | ng/g | 0.03079926295 | 0.09239778884 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E160 | Sea squirt | Diplosoma listerianum | vertebrate | tunicata | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C022 | NA | 22 | 1 | ng/g | 1.3200000 | NA | NA | Not available because sample size is one. | technical | 22 | 1 | 0.9600000 | NA | NA | technical | 3 | ng/g | 0.004661629686 | 0.01398488906 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E161 | Skate | Amblyraja hyperborea | vertebrate | tunicata | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | NA | 14 | 1 | ng/g | 1.0900000 | NA | NA | Not available because sample size is one. | technical | 14 | 1 | 0.0027709 | LOD | NA | technical | 4 | ng/g | 0.002770915071 | 0.008312745212 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E162 | Skate | Amblyraja hyperborea | vertebrate | tunicata | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | NA | 14 | 1 | ng/g | 1.5500000 | NA | NA | Not available because sample size is one. | technical | 14 | 1 | 1.3500000 | NA | NA | technical | 4 | ng/g | 0.01203365344 | 0.03610096033 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E163 | Skate | Amblyraja hyperborea | vertebrate | tunicata | NA | PFDoDA | 12 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | NA | 14 | 1 | ng/g | 1.3300000 | NA | NA | Not available because sample size is one. | technical | 14 | 1 | 0.0255728 | LOD | NA | technical | 4 | ng/g | 0.02557281543 | 0.07671844628 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E164 | Skate | Amblyraja hyperborea | vertebrate | tunicata | NA | PFTA | 14 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | NA | 14 | 1 | ng/g | 0.6700000 | NA | NA | Not available because sample size is one. | technical | 14 | 1 | 0.0070174 | LOD | NA | technical | 4 | ng/g | 0.007017439682 | 0.02105231905 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E165 | Skate | Amblyraja hyperborea | vertebrate | tunicata | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | NA | 14 | 1 | ng/g | 1.5100000 | NA | NA | Not available because sample size is one. | technical | 14 | 1 | 0.8800000 | NA | NA | technical | 4 | ng/g | 0.3642166626 | 1.092649988 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E166 | Yellow croaker | Larimichthys polyactis | vertebrate | tunicata | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C024 | NA | 35 | 1 | ng/g | 1.5700000 | NA | NA | Not available because sample size is one. | technical | 35 | 1 | 0.0179042 | LOD | NA | technical | 4 | ng/g | 0.0179042065 | 0.0537126195 | Shared control | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E167 | Yellow croaker | Larimichthys polyactis | vertebrate | tunicata | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C024 | NA | 35 | 1 | ng/g | 1.6800000 | NA | NA | Not available because sample size is one. | technical | 35 | 1 | 0.8900000 | NA | NA | technical | 4 | ng/g | 0.3768854178 | 1.130656253 | Shared control | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E168 | Yellow croaker | Larimichthys polyactis | vertebrate | tunicata | NA | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C025 | NA | 35 | 1 | ng/g | 1.5700000 | NA | NA | Not available because sample size is one. | technical | 35 | 1 | 2.1100000 | NA | NA | technical | 4 | ng/g | 0.0165860278 | 0.04975808341 | Shared control | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F005 | DelGobbo_2008 | 2008 | Canada | E169 | Yellow croaker | Larimichthys polyactis | vertebrate | tunicata | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C025 | NA | 35 | 1 | ng/g | 1.6800000 | NA | NA | Not available because sample size is one. | technical | 35 | 1 | 0.6800000 | NA | NA | technical | 4 | ng/g | 0.3921755285 | 1.176526586 | Shared control | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | NA |
| F006 | Hu_2020 | 2020 | China | E170 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFBA | 3 | NA | No | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5 | 5 | 5.3412073 | NA | 1.6889253 | biological | NA | ng/g | Not provided | 12.2 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | ML | 7.4193907 | 1.6889253 |
| F006 | Hu_2020 | 2020 | China | E171 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFOA | 8 | NA | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5 | 5 | 0.2674068 | NA | 0.0800584 | biological | NA | ng/g | Not provided | 0.226 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.1560332 | 0.0800584 |
| F006 | Hu_2020 | 2020 | China | E172 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFBS | 4 | NA | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5 | 5 | 23.9801208 | NA | 26.8453690 | biological | NA | ng/g | Not provided | 1.01 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 23.9889753 | 26.8453690 |
| F006 | Hu_2020 | 2020 | China | E173 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFOS | 8 | NA | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5 | 5 | 122.4133110 | NA | 62.4690572 | biological | NA | ng/g | Not provided | 1.57 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 39.4592027 | 62.4690572 |
| F006 | Hu_2020 | 2020 | China | E174 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFHpA | 7 | NA | No | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5 | 5 | 55.3995680 | NA | 55.3995680 | biological | NA | ng/g | Not provided | 0.47 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 30.6129835 | 55.3995680 |
| F006 | Hu_2020 | 2020 | China | E175 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFDoDA | 12 | NA | No | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5 | 5 | 2.2676991 | NA | 1.5334164 | biological | NA | ng/g | Not provided | 0.093 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.5599538 | 1.5334164 |
| F006 | Hu_2020 | 2020 | China | E176 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | PFHxS | 6 | NA | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5 | 5 | 0.8685897 | NA | 0.3034431 | biological | NA | ng/g | Not provided | 0.155 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 2.3827419 | 0.3034431 |
| F006 | Hu_2020 | 2020 | China | E177 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.380000 | FOSA | 8 | NA | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | NA | 5 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5 | 5 | 2.3838798 | NA | 1.2904183 | biological | NA | ng/g | Not provided | 0.026 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 1.6889253 | 1.2904183 |
| F006 | Hu_2020 | 2020 | China | E178 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFBA | 3 | NA | No | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5 | 5 | 4.9146982 | NA | 7.4344664 | biological | NA | ng/g | Not provided | 12.2 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 7.4193907 | 7.4344664 |
| F006 | Hu_2020 | 2020 | China | E179 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5 | 5 | 0.1932566 | NA | 0.0707998 | biological | NA | ng/g | Not provided | 0.226 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.1560332 | 0.0707998 |
| F006 | Hu_2020 | 2020 | China | E180 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFBS | 4 | NA | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5 | 5 | 10.8230680 | NA | 7.4606797 | biological | NA | ng/g | Not provided | 1.01 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 23.9889753 | 7.4606797 |
| F006 | Hu_2020 | 2020 | China | E181 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFOS | 8 | NA | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5 | 5 | 97.7348993 | NA | 23.1725546 | biological | NA | ng/g | Not provided | 1.57 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 39.4592027 | 23.1725546 |
| F006 | Hu_2020 | 2020 | China | E182 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFHpA | 7 | NA | No | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5 | 5 | 13.7149028 | NA | 23.6036055 | biological | NA | ng/g | Not provided | 0.47 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 30.6129835 | 23.6036055 |
| F006 | Hu_2020 | 2020 | China | E183 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFDoDA | 12 | NA | No | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5 | 5 | 2.3534292 | NA | 2.4839931 | biological | NA | ng/g | Not provided | 0.093 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.5599538 | 2.4839931 |
| F006 | Hu_2020 | 2020 | China | E184 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | PFHxS | 6 | NA | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5 | 5 | 0.6506410 | NA | 0.1079317 | biological | NA | ng/g | Not provided | 0.155 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 2.3827419 | 0.1079317 |
| F006 | Hu_2020 | 2020 | China | E185 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.480000 | FOSA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | NA | 5 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5 | 5 | 2.2540984 | NA | 1.2484167 | biological | NA | ng/g | Not provided | 0.026 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 1.6889253 | 1.2484167 |
| F006 | Hu_2020 | 2020 | China | E186 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFBA | 3 | NA | No | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5 | 5 | 7.9068241 | NA | 9.3812679 | biological | NA | ng/g | Not provided | 12.2 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 7.4193907 | 9.3812679 |
| F006 | Hu_2020 | 2020 | China | E187 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFOA | 8 | NA | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5 | 5 | 0.2308114 | NA | 0.1541468 | biological | NA | ng/g | Not provided | 0.226 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.1560332 | 0.1541468 |
| F006 | Hu_2020 | 2020 | China | E188 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFBS | 4 | NA | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5 | 5 | 9.8657220 | NA | 5.8014926 | biological | NA | ng/g | Not provided | 1.01 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 23.9889753 | 5.8014926 |
| F006 | Hu_2020 | 2020 | China | E189 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFOS | 8 | NA | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5 | 5 | 134.4379195 | NA | 58.0538019 | biological | NA | ng/g | Not provided | 1.57 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 39.4592027 | 58.0538019 |
| F006 | Hu_2020 | 2020 | China | E190 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFHpA | 7 | NA | No | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5 | 5 | 23.7041037 | NA | 35.9297367 | biological | NA | ng/g | Not provided | 0.47 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 30.6129835 | 35.9297367 |
| F006 | Hu_2020 | 2020 | China | E191 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFDoDA | 12 | NA | No | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5 | 5 | 2.8733407 | NA | 2.7470061 | biological | NA | ng/g | Not provided | 0.093 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.5599538 | 2.7470061 |
| F006 | Hu_2020 | 2020 | China | E192 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | PFHxS | 6 | NA | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5 | 5 | 1.1602564 | NA | 0.7375647 | biological | NA | ng/g | Not provided | 0.155 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 2.3827419 | 0.7375647 |
| F006 | Hu_2020 | 2020 | China | E193 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.140000 | FOSA | 8 | NA | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | NA | 5 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5 | 5 | 3.7500000 | NA | 3.7411362 | biological | NA | ng/g | Not provided | 0.026 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 1.6889253 | 3.7411362 |
| F006 | Hu_2020 | 2020 | China | E194 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFBA | 3 | NA | No | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5 | 5 | 4.8490814 | NA | 6.9303363 | biological | NA | ng/g | Not provided | 12.2 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 7.4193907 | 6.9303363 |
| F006 | Hu_2020 | 2020 | China | E195 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFOA | 8 | NA | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5 | 5 | 0.1652961 | NA | 0.0630496 | biological | NA | ng/g | Not provided | 0.226 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.1560332 | 0.0630496 |
| F006 | Hu_2020 | 2020 | China | E196 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFBS | 4 | NA | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5 | 5 | 7.5376305 | NA | 1.5022632 | biological | NA | ng/g | Not provided | 1.01 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 23.9889753 | 1.5022632 |
| F006 | Hu_2020 | 2020 | China | E197 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFOS | 8 | NA | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5 | 5 | 121.7142058 | NA | 62.5574247 | biological | NA | ng/g | Not provided | 1.57 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 39.4592027 | 62.5574247 |
| F006 | Hu_2020 | 2020 | China | E198 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFHpA | 7 | NA | No | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5 | 5 | 10.0971922 | NA | 16.4902451 | biological | NA | ng/g | Not provided | 0.47 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 30.6129835 | 16.4902451 |
| F006 | Hu_2020 | 2020 | China | E199 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFDoDA | 12 | NA | No | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5 | 5 | 2.9120575 | NA | 3.3602781 | biological | NA | ng/g | Not provided | 0.093 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 0.5599538 | 3.3602781 |
| F006 | Hu_2020 | 2020 | China | E200 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | PFHxS | 6 | NA | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5 | 5 | 0.8253205 | NA | 0.2542197 | biological | NA | ng/g | Not provided | 0.155 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 2.3827419 | 0.2542197 |
| F006 | Hu_2020 | 2020 | China | E201 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.310000 | FOSA | 8 | NA | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | NA | 5 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5 | 5 | 2.2814208 | NA | 0.4304018 | biological | NA | ng/g | Not provided | 0.026 | Shared control | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | NA | 1.6889253 | 0.4304018 |
| F007 | Kim_2020 | 2020 | Korea | E202 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. For volume of cooking liquid: 1 cup is 250 ml, accordingly for table spoon etc. | ML | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E203 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.1100000 | NA | 0.0000000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E204 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | NA | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E205 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0900000 | NA | 0.0600000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E206 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | NA | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | NA | 10 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E207 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | NA | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | NA | 10 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.1300000 | NA | 0.0400000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E208 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | NA | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | NA | 10 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.1400000 | NA | 0.0100000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E209 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | NA | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | NA | 10 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0900000 | NA | 0.0000000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E210 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | NA | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E211 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | NA | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E212 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | NA | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E213 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | NA | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | NA | 10 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E214 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | NA | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | NA | 10 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E215 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | NA | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | NA | 10 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E216 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | NA | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | NA | 10 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E217 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | NA | 10 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E218 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | NA | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | NA | 10 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0500000 | NA | 0.0000000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E219 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | NA | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | NA | 10 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0600000 | NA | 0.0200000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E220 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | NA | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | NA | 10 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E221 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | NA | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | NA | 10 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0600000 | NA | 0.0000000 | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E222 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | NA | Yes | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | NA | 10 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E223 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | NA | Yes | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | NA | 10 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E224 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | NA | Yes | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | NA | 10 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F007 | Kim_2020 | 2020 | Korea | E225 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | NA | Yes | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | NA | 10 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10 | 1 | 0.0200000 | LOD | NA | technical | NA | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Shared control | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | NA |
| F008 | Luo_2019 | 2019 | Korea | E316 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFOA | 8 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 20.7900000 | NA | 0.1700000 | sd | technical | 5 | 1 | 16.7700000 | NA | 0.4200000 | technical | NA | ng/g | 0.06 | 0.19 | Dependent | Table 4 | No | Scientific name of swimming crab not provided in paper, inferred as this species of swimming crab is commonly eaten in South korea (Kim, S., Lee, M.J., Lee, J.J., Choi, S.H. and Kim, B.S., 2017. Analysis of microbiota of the swimming crab (Portunus trituberculatus) in South Korea to identify risk markers for foodborne illness. LWT, 86, pp.483-491.) | NA | NA | NA |
| F008 | Luo_2019 | 2019 | Korea | E317 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFOS | 8 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.8100000 | NA | 0.0200000 | sd | technical | 5 | 1 | 0.7400000 | NA | 0.0300000 | technical | NA | ng/g | 0.07 | 0.07 | Dependent | Table 4 | No | NA | NA | NA | NA |
| F008 | Luo_2019 | 2019 | Korea | E318 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFBA | 3 | NA | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.1400000 | NA | 0.0100000 | sd | technical | 5 | 1 | 0.0400000 | NA | 0.0100000 | technical | NA | ng/g | 0.06 | 0.19 | Dependent | Table 4 | No | NA | NA | NA | NA |
| F008 | Luo_2019 | 2019 | Korea | E319 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFHpA | 7 | NA | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.3700000 | NA | 0.0300000 | sd | technical | 5 | 1 | 0.3200000 | NA | 0.0100000 | technical | NA | ng/g | 0.06 | 0.17 | Dependent | Table 4 | No | NA | NA | NA | NA |
| F008 | Luo_2019 | 2019 | Korea | E320 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFNA | 9 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 2.8900000 | NA | 0.0200000 | sd | technical | 5 | 1 | 2.3000000 | NA | 0.0300000 | technical | NA | ng/g | 0.03 | 0.08 | Dependent | Table 4 | No | NA | NA | NA | NA |
| F008 | Luo_2019 | 2019 | Korea | E321 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFDA | 10 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.6600000 | NA | 0.0200000 | sd | technical | 5 | 1 | 0.5700000 | NA | 0.0200000 | technical | NA | ng/g | 0.04 | 0.11 | Dependent | Table 4 | No | NA | NA | NA | NA |
| F008 | Luo_2019 | 2019 | Korea | E322 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.9300000 | NA | 0.0100000 | sd | technical | 5 | 1 | 0.7900000 | NA | 0.0200000 | technical | NA | ng/g | 0.08 | 0.25 | Dependent | Table 4 | No | NA | NA | NA | NA |
| F008 | Luo_2019 | 2019 | Korea | E323 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFDoDA | 12 | NA | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.2500000 | NA | 0.0200000 | sd | technical | 5 | 1 | 0.2300000 | NA | 0.0100000 | technical | NA | ng/g | 0.06 | 0.19 | Dependent | Table 4 | No | NA | NA | NA | NA |
| F008 | Luo_2019 | 2019 | Korea | E324 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFTrA | 13 | NA | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 1.1200000 | NA | 0.0600000 | sd | technical | 5 | 1 | 1.3800000 | NA | 0.0900000 | technical | NA | ng/g | 0.05 | 0.16 | Dependent | Table 4 | No | NA | NA | NA | NA |
| F008 | Luo_2019 | 2019 | Korea | E325 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFTA | 14 | NA | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.2800000 | NA | 0.0100000 | sd | technical | 5 | 1 | 0.2600000 | NA | 0.0200000 | technical | NA | ng/g | 0.05 | 0.15 | Dependent | Table 4 | No | NA | NA | NA | NA |
| F008 | Luo_2019 | 2019 | Korea | E326 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFHxS | 6 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.4800000 | NA | 0.0300000 | sd | technical | 5 | 1 | 0.3300000 | NA | 0.0300000 | technical | NA | ng/g | 0.08 | 0.25 | Dependent | Table 4 | No | NA | NA | NA | NA |
| F008 | Luo_2019 | 2019 | Korea | E327 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFDS | 10 | NA | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 0.0400000 | NA | 0.0100000 | sd | technical | 5 | 1 | 0.0400000 | NA | 0.0100000 | technical | NA | ng/g | 0.09 | 0.27 | Dependent | Table 4 | No | NA | NA | NA | NA |
| F008 | Luo_2019 | 2019 | Korea | E328 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | FOSA | 8 | NA | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | NA | 5 | 1 | ng/g | 1.5400000 | NA | 0.0900000 | sd | technical | 5 | 1 | 2.5500000 | NA | 0.1900000 | technical | NA | ng/g | 0.04 | 0.11 | Dependent | Table 4 | No | NA | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E329 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C041 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1590000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | ML - note shared controls for differend cooking times and methods | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E330 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C042 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1170000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E331 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C043 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0790000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E332 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C044 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1420000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E333 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C045 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1160000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E334 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C046 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0980000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E335 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C047 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1400000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E336 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C048 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1330000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E337 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C049 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0710000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E338 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C050 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2010000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E339 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C051 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0590000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E340 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C052 | NA | 10 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0480000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E341 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C041 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 14.7000000 | NA | 0.0090000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E342 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C042 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 9.3500000 | NA | 0.0080000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E343 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C043 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 3.6600000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E344 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C044 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 5.6300000 | NA | 0.0050000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E345 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C045 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 4.5000000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E346 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C046 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 3.7700000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E347 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C047 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 8.2800000 | NA | 0.0070000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E348 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C048 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 6.6200000 | NA | 0.0060000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E349 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C049 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 3.4800000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E350 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C050 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 4.4900000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E351 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C051 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 3.0500000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E352 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C052 | NA | 10 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10 | 1 | 2.8300000 | NA | 0.0030000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E353 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C053 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1960000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E354 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C054 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1180000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E355 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C055 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0840000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E356 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C056 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2030000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E357 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C057 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1390000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E358 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C058 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1040000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E359 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C059 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2070000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E360 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C060 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0970000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E361 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C061 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0820000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E362 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C062 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1960000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E363 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C063 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0510000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E364 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C064 | NA | 10 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2550000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E365 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C053 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 4.7800000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E366 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C054 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 3.5000000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E367 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C055 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 1.5100000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E368 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C056 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 7.0500000 | NA | 0.0060000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E369 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C057 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 2.4700000 | NA | 0.0030000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E370 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C058 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 1.7600000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E371 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C059 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 3.0300000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E372 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C060 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 2.0400000 | NA | 0.0030000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E373 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C061 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 1.2300000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E374 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C062 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 4.2800000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E375 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C063 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 2.7800000 | NA | 0.0030000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E376 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C064 | NA | 10 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10 | 1 | 1.0200000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E377 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C065 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2420000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E378 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C066 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1870000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E379 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C067 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0960000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E380 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C068 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1750000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E381 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C069 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1530000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E382 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C070 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0980000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E383 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C071 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1890000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E384 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C072 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1320000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E385 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C073 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0930000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E386 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C074 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1810000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E387 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C075 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0880000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E388 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C076 | NA | 10 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0660000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E389 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C065 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 4.1500000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E390 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C066 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 2.6500000 | NA | 0.0030000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E391 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C067 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 1.2300000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E392 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C068 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 4.4400000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E393 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C069 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 2.3600000 | NA | 0.0030000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E394 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C070 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 1.6500000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E395 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C071 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 3.6800000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E396 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C072 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 1.7300000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E397 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C073 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 0.9200000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E398 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C074 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 4.0300000 | NA | 0.0040000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E399 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C075 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 1.9700000 | NA | 0.0020000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E400 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C076 | NA | 10 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10 | 1 | 0.8400000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E401 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C077 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2020000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E402 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C078 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1280000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E403 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C079 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0920000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E404 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C080 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1580000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E405 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C081 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1210000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E406 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C082 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0980000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E407 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C083 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1680000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E408 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C084 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1340000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E409 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C085 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0910000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E410 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C086 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1740000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E411 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C087 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0960000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E412 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C088 | NA | 10 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0440000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E413 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C077 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2760000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E414 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C078 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1750000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E415 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C079 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0900000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E416 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C080 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3110000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E417 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C081 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2840000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E418 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C082 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0940000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E419 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C083 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2970000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E420 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C084 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1610000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E421 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C085 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0850000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E422 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C086 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1640000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E423 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C087 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0930000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E424 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C088 | NA | 10 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0670000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E425 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C089 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1970000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E426 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C090 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1460000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E427 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C091 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0900000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E428 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C092 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2120000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E429 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C093 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1220000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E430 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C094 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0940000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E431 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C095 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1470000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E432 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C096 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1280000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E433 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C097 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0690000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E434 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C098 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1450000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E435 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C099 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1020000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E436 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C100 | NA | 10 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0420000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E437 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C089 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3720000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E438 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C090 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2510000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E439 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C091 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0940000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E440 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C092 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2540000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E441 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C093 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1800000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E442 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C094 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0970000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E443 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C095 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3260000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E444 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C096 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1550000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E445 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C097 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0630000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E446 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C098 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3580000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E447 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C099 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1970000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E448 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C100 | NA | 10 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0560000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E449 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C101 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1470000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E450 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C102 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1150000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E451 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C103 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0500000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E452 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C104 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1480000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E453 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C105 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1070000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E454 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | oil-based | NA | 160 | 1200 | No | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C106 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0570000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E455 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C107 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1210000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E456 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C108 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0950000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E457 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C109 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0430000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E458 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C110 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1150000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E459 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C111 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0820000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E460 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C112 | NA | 10 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0330000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E461 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C101 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.6640000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E462 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C102 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3120000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E463 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C103 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0990000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E464 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C104 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.6180000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E465 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C105 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3780000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E466 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C106 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1070000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E467 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C107 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.5980000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E468 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C108 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.4020000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E469 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C109 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0970000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E470 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C110 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.6180000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E471 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C111 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2460000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E472 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C112 | NA | 10 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0890000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E473 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C113 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0980000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E474 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C114 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0620000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E475 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C115 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0430000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E476 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C116 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0800000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E477 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C117 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0600000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E478 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C118 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0450000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E479 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C119 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0980000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E480 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C120 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0700000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E481 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C121 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0340000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E482 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C122 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0650000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E483 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C123 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0580000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E484 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C124 | NA | 10 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0320000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E485 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C113 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1540000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E486 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C114 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1080000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E487 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C115 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0920000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E488 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C116 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1470000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E489 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C117 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1020000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E490 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C118 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0940000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E491 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C119 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1260000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E492 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C120 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0990000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E493 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C121 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0520000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E494 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C122 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1020000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E495 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C123 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0760000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E496 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C124 | NA | 10 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0490000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E497 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C125 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1450000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E498 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C126 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1130000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E499 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C127 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0540000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E500 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C128 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1520000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E501 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C129 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1280000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E502 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C130 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0610000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E503 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C131 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1220000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E504 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C132 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0920000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E505 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C133 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0490000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E506 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C134 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1180000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E507 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C135 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0890000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E508 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C136 | NA | 10 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0440000 | NA | 0.0010000 | technical | 3 | ng/g | 0.009 | 0.030 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E509 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C125 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3570000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E510 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C126 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2100000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E511 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C127 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0920000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E512 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C128 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.2560000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E513 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C129 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1840000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E514 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C130 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0990000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E515 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C131 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3440000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E516 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C132 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1480000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E517 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C133 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0820000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E518 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C134 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.3410000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E519 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C135 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.1920000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F010 | Sungur_2019 | 2019 | Turkey | E520 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C136 | NA | 10 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10 | 1 | 0.0540000 | NA | 0.0010000 | technical | 3 | ng/g | 0.006 | 0.020 | Shared control | Table 3 | No | Authors replied | NA | NA | NA |
| F011 | Taylor_2019 | 2019 | Australia | E521 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.470000 | PFHxS | 6 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Contaminated site | 4 | 4 | ng/g | 0.9673000 | NA | 1.0026000 | sd | biological | 4 | 4 | 1.4745000 | NA | 1.7430000 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | ML - check empty fields, why SE/SD field is NA? | 1.0026000 | 1.7430000 |
| F011 | Taylor_2019 | 2019 | Australia | E522 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.470000 | PFOS | 8 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Contaminated site | 6 | 6 | ng/g | 75.6360000 | NA | 133.7000000 | sd | biological | 6 | 6 | 84.5499000 | NA | 130.5000000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 133.7000000 | 130.5000000 |
| F011 | Taylor_2019 | 2019 | Australia | E523 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.470000 | PFOS | 8 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.4610420 | 43.3800 | C138 | Clean site | 3 | 3 | ng/g | 0.0894000 | NA | 0.0339000 | sd | biological | 3 | 3 | 0.1210000 | NA | 0.0390000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0339000 | 0.0390000 |
| F011 | Taylor_2019 | 2019 | Australia | E526 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.470000 | PFDS | 10 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Contaminated site | 2 | 2 | ng/g | 0.1391000 | NA | 0.0247000 | sd | biological | 2 | 2 | 0.3760000 | NA | 0.0240000 | biological | 1 | ng/g | 0.030122517 | 0.10040839 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0247000 | 0.0240000 |
| F011 | Taylor_2019 | 2019 | Australia | E527 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.470000 | FOSA | 8 | NA | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Contaminated site | 2 | 2 | ng/g | 0.0749000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 2 | 2 | 0.1985000 | NA | 0.0120000 | biological | 1 | ng/g | 0.034582913 | 0.115276378 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | 0.0120000 |
| F011 | Taylor_2019 | 2019 | Australia | E528 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.640000 | PFHxS | 6 | linear | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.7696748 | 51.9700 | C140 | Contaminated site | 5 | 5 | ng/g | 0.7841000 | NA | 0.9602000 | sd | biological | 5 | 5 | 0.8414000 | NA | 1.0420000 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.9602000 | 1.0420000 |
| F011 | Taylor_2019 | 2019 | Australia | E529 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.640000 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.7696748 | 51.9700 | C139 | Contaminated site | 6 | 6 | ng/g | 75.6360000 | NA | 133.7000000 | sd | biological | 6 | 6 | 70.8427000 | NA | 106.0000000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 133.7000000 | 106.0000000 |
| F011 | Taylor_2019 | 2019 | Australia | E530 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.640000 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.9220839 | 43.3800 | C140 | Clean site | 2 | 2 | ng/g | 0.1090000 | NA | 0.0014000 | sd | biological | 2 | 2 | 0.2005000 | NA | 0.0730000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0014000 | 0.0730000 |
| F011 | Taylor_2019 | 2019 | Australia | E533 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.640000 | FOSA | 8 | NA | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.7696748 | 51.9700 | C139 | Contaminated site | 4 | 4 | ng/g | 0.1070000 | NA | 0.0397000 | sd | biological | 4 | 4 | 0.2540000 | NA | 0.1320000 | biological | 1 | ng/g | 0.034582913 | 0.115276378 | Shared control | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0397000 | 0.1320000 |
| F011 | Taylor_2019 | 2019 | Australia | E534 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHxA | 6 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 3 | 3 | ng/g | 0.1513000 | NA | 0.0306000 | sd | biological | 3 | 3 | 0.0729200 | NA | 0.0210000 | biological | 1 | ng/g | 0.028099467 | 0.093664888 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0306000 | 0.0210000 |
| F011 | Taylor_2019 | 2019 | Australia | E535 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHpA | 7 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 6 | 6 | ng/g | 0.2070000 | NA | 0.1445000 | sd | biological | 6 | 6 | 0.1086500 | NA | 0.0520000 | biological | 1 | ng/g | 0.01867491 | 0.0622497 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.1445000 | 0.0520000 |
| F011 | Taylor_2019 | 2019 | Australia | E536 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOA | 8 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 6 | 6 | ng/g | 0.4279000 | NA | 0.2601000 | sd | biological | 6 | 6 | 0.2316000 | NA | 0.1070000 | biological | 1 | ng/g | 0.014519809 | 0.048399364 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.2601000 | 0.1070000 |
| F011 | Taylor_2019 | 2019 | Australia | E537 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOA | 8 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | Clean site | 4 | 4 | ng/g | 0.0433000 | NA | 0.0137000 | sd | biological | 4 | 4 | 0.0712200 | NA | 0.0660000 | biological | 1 | ng/g | 0.014519809 | 0.048399364 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0137000 | 0.0660000 |
| F011 | Taylor_2019 | 2019 | Australia | E538 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFUnDA | 11 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 4 | 4 | ng/g | 0.1128000 | NA | 0.0093000 | sd | biological | 4 | 4 | 0.0579700 | <LOQ | NA | NA | 1 | ng/g | 0.026755217 | 0.089184057 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0093000 | NA |
| F011 | Taylor_2019 | 2019 | Australia | E539 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFUnDA | 11 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | Clean site | 1 | 1 | ng/g | 0.1047000 | NA | NA | sd | biological | 1 | 1 | 0.0579700 | <LOQ | NA | NA | 1 | ng/g | 0.026755217 | 0.089184057 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | NA |
| F011 | Taylor_2019 | 2019 | Australia | E540 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFDoDA | 12 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 1 | 1 | ng/g | 0.0802000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 1 | 1 | 0.1279700 | No sd, as N = 1 | NA | NA | 1 | ng/g | 0.037026547 | 0.123421824 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | NA |
| F011 | Taylor_2019 | 2019 | Australia | E541 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFDoDA | 12 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | Clean site | 1 | 1 | ng/g | 0.1230000 | NA | NA | sd | biological | 1 | 1 | 0.0802200 | <LOQ | NA | NA | 1 | ng/g | 0.037026547 | 0.123421824 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | NA |
| F011 | Taylor_2019 | 2019 | Australia | E542 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHxS | 6 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 6 | 6 | ng/g | 0.5991000 | NA | 0.2053000 | sd | biological | 6 | 6 | 0.3865700 | NA | 0.0790000 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.2053000 | 0.0790000 |
| F011 | Taylor_2019 | 2019 | Australia | E543 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHxS | 6 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | Clean site | 1 | 1 | ng/g | 0.1230000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 1 | 1 | 0.0809900 | No sd, as N = 1 | NA | NA | 1 | ng/g | 0.023508736 | 0.078362453 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | NA |
| F011 | Taylor_2019 | 2019 | Australia | E544 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOS | 8 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 6 | 6 | ng/g | 5.0500000 | NA | 0.4637000 | sd | biological | 6 | 6 | 5.5333300 | NA | 0.8290000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.4637000 | 0.8290000 |
| F011 | Taylor_2019 | 2019 | Australia | E545 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOS | 8 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | Clean site | 6 | 6 | ng/g | 0.1917000 | NA | 0.2129000 | sd | biological | 6 | 6 | 0.1917100 | NA | 0.2360000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.2129000 | 0.2360000 |
| F011 | Taylor_2019 | 2019 | Australia | E548 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | FOSA | 8 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | Contaminated site | 6 | 6 | ng/g | 0.3112000 | NA | 0.1413000 | sd | biological | 6 | 6 | 0.3215300 | NA | 0.0990000 | biological | 1 | ng/g | 0.034582913 | 0.115276378 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.1413000 | 0.0990000 |
| F011 | Taylor_2019 | 2019 | Australia | E549 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFHpA | 7 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | Contaminated site | 10 | 1 | ng/g | 0.0802000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 10 | 1 | 0.1279700 | NA | NA | biological | 1 | ng/g | 0.01867491 | 0.0622497 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | NA |
| F011 | Taylor_2019 | 2019 | Australia | E550 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFOA | 8 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | Contaminated site | 10 | 1 | ng/g | 0.2229000 | NA | 0.0668000 | sd | biological | 60 | 6 | 0.4689700 | NA | 0.1040000 | biological | 1 | ng/g | 0.014519809 | 0.048399364 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0668000 | 0.1040000 |
| F011 | Taylor_2019 | 2019 | Australia | E551 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFNA | 9 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | Contaminated site | 10 | 1 | ng/g | 0.0910000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 60 | 6 | 0.2330900 | NA | 0.0370000 | biological | 1 | ng/g | 0.036013573 | 0.120045244 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | 0.0370000 |
| F011 | Taylor_2019 | 2019 | Australia | E552 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFDA | 10 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | Contaminated site | 10 | 1 | ng/g | 0.0854000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 50 | 5 | 0.1877100 | NA | 0.0530000 | biological | 1 | ng/g | 0.039417906 | 0.131393021 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | 0.0530000 |
| F011 | Taylor_2019 | 2019 | Australia | E553 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFHxS | 6 | linear | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | Contaminated site | 10 | 1 | ng/g | 2.3305000 | NA | 1.3905000 | sd | biological | 60 | 6 | 6.3161900 | NA | 1.6280000 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 1.3905000 | 1.6280000 |
| F011 | Taylor_2019 | 2019 | Australia | E554 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFOS | 8 | linear | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | Contaminated site | 10 | 1 | ng/g | 7.4167000 | NA | 2.8414000 | sd | biological | 60 | 6 | 16.1667000 | NA | 3.8690000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 2.8414000 | 3.8690000 |
| F011 | Taylor_2019 | 2019 | Australia | E555 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFOS | 8 | linear | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 12.5376128 | 39.8800 | C144 | Clean site | 10 | 1 | ng/g | 0.0562000 | NA | 0.0133000 | sd | biological | 50 | 5 | 0.1180000 | NA | 0.0290000 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.0133000 | 0.0290000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E557 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 72.739187 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.9000000 | 333.3333 | C145 | NA | 23 | 1 | ng/g | 1.5000000 | NA | 0.0400000 | sd | technical | 30 | 1 | 1.7500000 | NA | 0.0500000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper) | ML - why “Se_technical_biological” is coded as “sd”? “If_technical_how_many” needs a number. Shared control between differend cooking methods | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E558 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 72.739187 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.9000000 | 333.3333 | C145 | NA | 23 | 1 | ng/g | 1.8600000 | NA | 0.1900000 | sd | technical | 30 | 1 | 2.9900000 | NA | 0.2200000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E559 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 72.739187 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.9000000 | 333.3333 | C145 | NA | 23 | 1 | ng/g | 3.0600000 | NA | 0.1000000 | sd | technical | 30 | 1 | 6.6200000 | NA | 0.1400000 | technical | 1 | ng/g | 0.49 | 1.48 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E560 | Bogue | Boops boops | vertebrate | marine fish | 18.354430 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3600000 | 833.3333 | C146 | NA | 12 | 1 | ng/g | 0.2400000 | NA | 0.0300000 | sd | technical | 30 | 1 | 0.4400000 | NA | 0.0200000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E561 | Bogue | Boops boops | vertebrate | marine fish | 18.354430 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3600000 | 833.3333 | C146 | NA | 12 | 1 | ng/g | 0.5600000 | NA | 0.0800000 | sd | technical | 30 | 1 | 1.1200000 | NA | 0.0300000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E562 | Bogue | Boops boops | vertebrate | marine fish | 18.354430 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3600000 | 833.3333 | C146 | NA | 12 | 1 | ng/g | 0.8200000 | NA | 0.0400000 | sd | technical | 30 | 1 | 1.2700000 | NA | 0.0600000 | technical | 1 | ng/g | 0.49 | 1.48 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E563 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.000000 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | NA | 20 | 1 | ng/g | 0.4200000 | NA | 0.0500000 | sd | technical | 10 | 1 | 0.7000000 | LOD | NA | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E564 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.000000 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | NA | 20 | 1 | ng/g | 0.6200000 | NA | 0.0800000 | sd | technical | 10 | 1 | 0.1000000 | <LOD | NA | NA | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E565 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.000000 | PFBS | 4 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | NA | 20 | 1 | ng/g | 0.4500000 | NA | 0.0700000 | sd | technical | 10 | 1 | 0.8300000 | NA | 0.0300000 | technical | 1 | ng/g | 0.57 | 1.7 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E566 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.000000 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | NA | 20 | 1 | ng/g | 0.8400000 | NA | 0.1000000 | sd | technical | 10 | 1 | 1.2400000 | NA | 0.0600000 | technical | 1 | ng/g | 0.49 | 1.48 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E567 | Picarel | Spicara smaris | vertebrate | marine fish | 44.037940 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4700000 | 638.2979 | C148 | NA | 20 | 1 | ng/g | 0.7000000 | NA | 0.0900000 | sd | technical | 30 | 1 | 1.3500000 | NA | 0.0800000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E568 | Picarel | Spicara smaris | vertebrate | marine fish | 44.037940 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4700000 | 638.2979 | C148 | NA | 20 | 1 | ng/g | 20.3700000 | NA | 2.4700000 | sd | technical | 30 | 1 | 44.6900000 | NA | 3.9300000 | technical | 1 | ng/g | 0.49 | 1.48 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E569 | Sand smelt | Atherina boyeri | vertebrate | marine fish | 79.108280 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5200000 | 576.9231 | C149 | NA | 39 | 1 | ng/g | 0.3500000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 30 | 1 | 0.7400000 | NA | 0.0900000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E570 | Sand smelt | Atherina boyeri | vertebrate | marine fish | 79.108280 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5200000 | 576.9231 | C149 | NA | 39 | 1 | ng/g | 1.0800000 | NA | 0.0300000 | sd | technical | 30 | 1 | 1.9800000 | NA | 0.0400000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E571 | Sand smelt | Atherina boyeri | vertebrate | marine fish | 79.108280 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5200000 | 576.9231 | C149 | NA | 39 | 1 | ng/g | 1.1600000 | NA | 0.0500000 | sd | technical | 30 | 1 | 3.0100000 | NA | 0.1300000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E572 | Sardine | Sardina pilchardus | vertebrate | marine fish | 57.258065 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.8800000 | 340.9091 | C150 | NA | 14 | 1 | ng/g | 0.1000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 30 | 1 | 0.9300000 | NA | 0.0300000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E573 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.316212 | PFNA | 9 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | NA | 15 | 1 | ng/g | 0.6000000 | NA | 0.0300000 | sd | technical | 30 | 1 | 0.5700000 | NA | 0.1100000 | technical | 1 | ng/g | 0.42 | 1.25 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E574 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.316212 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | NA | 15 | 1 | ng/g | 0.6500000 | NA | 0.0600000 | sd | technical | 30 | 1 | 0.5600000 | NA | 0.0700000 | technical | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E575 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.316212 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | NA | 15 | 1 | ng/g | 1.0500000 | NA | 0.1300000 | sd | technical | 30 | 1 | 0.7300000 | NA | 0.2000000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E576 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.316212 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | NA | 15 | 1 | ng/g | 0.1000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | technical | 30 | 1 | 1.3800000 | NA | 0.0700000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E577 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.316212 | PFOS | 8 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | NA | 15 | 1 | ng/g | 5.6600000 | NA | 0.1500000 | sd | technical | 30 | 1 | 0.1000000 | <LOD | NA | NA | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E578 | Shrimp | Parapenaeus longirostris | vertebrate | marine fish | NA | PFPeA | 5 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 4.9400000 | NA | 0.2600000 | sd | technical | 40 | 1 | 14.8800000 | NA | 1.6100000 | technical | 1 | ng/g | 0.39 | 1.17 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E579 | Shrimp | Parapenaeus longirostris | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 0.3000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | technical | 40 | 1 | 0.9900000 | NA | 0.2100000 | technical | 1 | ng/g | 0.6 | 1.82 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E580 | Shrimp | Parapenaeus longirostris | vertebrate | marine fish | NA | PFNA | 9 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 1.2700000 | NA | 0.0700000 | sd | technical | 40 | 1 | 1.5200000 | NA | 0.1100000 | technical | 1 | ng/g | 0.42 | 1.25 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E581 | Shrimp | Parapenaeus longirostris | vertebrate | marine fish | NA | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 1.7300000 | NA | 0.0800000 | sd | technical | 40 | 1 | 1.8100000 | NA | 0.1900000 | technical | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E582 | Shrimp | Parapenaeus longirostris | vertebrate | marine fish | NA | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 2.7600000 | NA | 0.2100000 | sd | technical | 40 | 1 | 6.8200000 | NA | 0.2200000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E583 | Shrimp | Parapenaeus longirostris | vertebrate | marine fish | NA | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 1.3600000 | NA | 0.0900000 | sd | technical | 40 | 1 | 2.3100000 | NA | 0.0900000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E584 | Shrimp | Parapenaeus longirostris | vertebrate | marine fish | NA | PFBS | 4 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 1.3700000 | NA | 0.1600000 | sd | technical | 40 | 1 | 0.2850000 | <LOD | NA | NA | 1 | ng/g | 0.57 | 1.7 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E585 | Shrimp | Parapenaeus longirostris | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | NA | 30 | 1 | ng/g | 5.1500000 | NA | 0.3900000 | sd | technical | 40 | 1 | 8.0200000 | NA | 0.4200000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E586 | Squid | Loligo vulgaris | vertebrate | marine fish | 47.867299 | PFPeA | 5 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | NA | 18 | 1 | ng/g | 0.1950000 | <LOD | NA | sd | technical | 40 | 1 | 5.0600000 | NA | 0.1900000 | technical | 1 | ng/g | 0.39 | 1.17 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E587 | Squid | Loligo vulgaris | vertebrate | marine fish | 47.867299 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | NA | 18 | 1 | ng/g | 0.3450000 | <LOD | NA | sd | technical | 40 | 1 | 0.5100000 | NA | 0.0400000 | technical | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E588 | Squid | Loligo vulgaris | vertebrate | marine fish | 47.867299 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | NA | 18 | 1 | ng/g | 0.3500000 | <LOD | NA | sd | technical | 40 | 1 | 1.0400000 | NA | 0.0200000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E589 | Squid | Loligo vulgaris | vertebrate | marine fish | 47.867299 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | NA | 18 | 1 | ng/g | 0.1000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 40 | 1 | 1.6500000 | NA | 0.0700000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E590 | Squid | Loligo vulgaris | vertebrate | marine fish | 47.867299 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | NA | 18 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 40 | 1 | 1.5600000 | NA | 0.1700000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E591 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.158585 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | NA | 30 | 1 | ng/g | 0.3450000 | <LOD | NA | sd | technical | 30 | 1 | 0.8300000 | NA | 0.0100000 | technical | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E592 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.158585 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | NA | 30 | 1 | ng/g | 1.5000000 | NA | 0.0400000 | sd | technical | 30 | 1 | 2.7300000 | NA | 0.1300000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E593 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.158585 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | NA | 30 | 1 | ng/g | 1.8600000 | NA | 0.1900000 | sd | technical | 30 | 1 | 3.5200000 | NA | 0.1000000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E594 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.158585 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | NA | 30 | 1 | ng/g | 3.0600000 | NA | 0.1000000 | sd | technical | 30 | 1 | 6.2900000 | NA | 0.3400000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E595 | Bogue | Boops boops | vertebrate | marine fish | 7.436709 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C155 | NA | 30 | 1 | ng/g | 0.2400000 | NA | 0.0300000 | sd | technical | 30 | 1 | 0.4300000 | NA | 0.0300000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E596 | Bogue | Boops boops | vertebrate | marine fish | 7.436709 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C155 | NA | 30 | 1 | ng/g | 0.5600000 | NA | 0.0800000 | sd | technical | 30 | 1 | 0.6300000 | NA | 0.0200000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E597 | Bogue | Boops boops | vertebrate | marine fish | 7.436709 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C155 | NA | 30 | 1 | ng/g | 0.8200000 | NA | 0.0400000 | sd | technical | 30 | 1 | 0.8700000 | NA | 0.0700000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E598 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.909091 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | NA | 10 | 1 | ng/g | 0.3450000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 10 | 1 | 0.8200000 | NA | 0.0300000 | technical | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E599 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.909091 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | NA | 10 | 1 | ng/g | 0.4200000 | NA | 0.0500000 | sd | technical | 10 | 1 | 1.1100000 | NA | 0.1500000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E600 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.909091 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | NA | 10 | 1 | ng/g | 0.6200000 | NA | 0.0800000 | sd | technical | 10 | 1 | 1.8900000 | NA | 0.0500000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E601 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.909091 | PFBS | 4 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | NA | 10 | 1 | ng/g | 0.4500000 | NA | 0.0700000 | sd | technical | 10 | 1 | 0.2850000 | <LOD | NA | NA | 1 | ng/g | 0.57 | 1.7 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E602 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.909091 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | NA | 10 | 1 | ng/g | 0.8400000 | NA | 0.1000000 | sd | technical | 10 | 1 | 2.4000000 | NA | 0.1300000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E603 | Sardine | Sardina pilchardus | vertebrate | marine fish | 9.946237 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C157 | NA | 30 | 1 | ng/g | 0.3450000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 30 | 1 | 0.8700000 | NA | 0.0300000 | technical | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E604 | Sardine | Sardina pilchardus | vertebrate | marine fish | 9.946237 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C157 | NA | 30 | 1 | ng/g | 0.3500000 | <LOD | NA | sd | technical | 30 | 1 | 1.7000000 | NA | 0.1300000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E605 | Sardine | Sardina pilchardus | vertebrate | marine fish | 9.946237 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C157 | NA | 30 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 30 | 1 | 3.1900000 | NA | 0.0900000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E606 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.656501 | PFNA | 9 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | NA | 30 | 1 | ng/g | 0.6000000 | NA | 0.0300000 | sd | technical | 30 | 1 | 0.5000000 | NA | 0.0500000 | technical | 1 | ng/g | 0.42 | 1.25 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E607 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.656501 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | NA | 30 | 1 | ng/g | 0.6500000 | NA | 0.0600000 | sd | technical | 30 | 1 | 0.3450000 | <LOD | NA | NA | 1 | ng/g | 0.69 | 2.08 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E608 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.656501 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | NA | 30 | 1 | ng/g | 1.0500000 | NA | 0.1300000 | sd | technical | 30 | 1 | 0.8200000 | NA | 0.0200000 | technical | 1 | ng/g | 0.7 | 2.11 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E609 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.656501 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | NA | 30 | 1 | ng/g | 5.6600000 | NA | 0.1500000 | sd | technical | 30 | 1 | 10.2300000 | NA | 0.5300000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E610 | Squid | Loligo vulgaris | vertebrate | mollusca | 24.289099 | PFOA | 8 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C159 | NA | 40 | 1 | ng/g | 0.3000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 40 | 1 | 0.4000000 | NA | 0.0100000 | technical | 1 | ng/g | 0.6 | 1.82 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E611 | Squid | Loligo vulgaris | vertebrate | mollusca | 24.289099 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C159 | NA | 40 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 40 | 1 | 1.0900000 | NA | 0.0200000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
| F013 | Vassiliadou_2015 | 2015 | Greece | E612 | Squid | Loligo vulgaris | vertebrate | mollusca | 24.289099 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C159 | NA | 40 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 40 | 1 | 1.1900000 | NA | 0.1700000 | technical | 1 | ng/g | 0.2 | 0.59 | Shared control | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author | NA | NA | NA |
The phylogenetic tree was generated in the tree_cooked_fish_MA.Rmd document
tree <- read.tree(here("data", "plot_cooked_fish_MA.tre")) # Import phylogenetic tree (see tree_cooked_fish_MA.Rmd for more details)
tree <- compute.brlen(tree) # Generate branch lengths
cor_tree <- vcv(tree,corr = T) # Generate phylogenetic variance-covariance matrix
dat$Phylogeny <- str_replace(dat$Species_Scientific, " ", "_") # Add the `phylogeny` column to the data frame
colnames(cor_tree) %in% dat$Phylogeny # Check correspondence between tip names and data frame## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
plot(tree)The average coefficient of variation in PFAS concentration was calculated for each study and treatment, according to Doncaster and Spake (2018) Correction for bias in meta-analysis of little-replicated studies. Methods in Ecology and Evolution; 9:634-644. Then, these values were averaged across studies and used to calculate the lnRR corrected for small sample sizes (for formula, see the lnRR_func above)
aCV2 <- dat %>%
group_by(Study_ID) %>% # Group by study
summarise(CV2c = mean((SDc/Mc)^2, na.rm = T), # Calculate the squared coefficient of variation for control and experimental groups
CV2e = mean((SDe/Me)^2, na.rm = T)) %>%
ungroup() %>% # ungroup
summarise(aCV2c = mean(CV2c, na.rm = T), # Mean CV^2 for exp and control groups across studies
aCV2e = mean(CV2e, na.rm = T))
effect <- lnRR_func(Mc = dat$Mc,
Nc = dat$Nc,
Me = dat$Me,
Ne = dat$Ne,
aCV2c = aCV2[[1]],
aCV2e = aCV2[[2]],
rho = 0.5) # Calculate effect sizes
dat <- dat %>%
mutate(N_tilde = (Nc*Ne)/(Nc + Ne)) # Calculate the effective sample size
dat <- cbind(dat, effect) # Merge effect sizes with the data frame
VCV_lnRR <- make_VCV_matrix(dat, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # Because some effect sizes share the same control, we generated a variance-covariance matrix to account for correlated errors (i.e. effectively dividing the weight of the correlated estimates by half)# mean
ggplot(dat, aes(x=lnRR))+ geom_histogram(fill = "salmon", col = "black", binwidth = 0.2) + theme_classic()# variance
ggplot(dat, aes(x=var_lnRR))+ geom_histogram(fill = "salmon", col = "black", binwidth = 0.05) + theme_classic()# log variance
ggplot(dat, aes(x=var_lnRR))+ geom_histogram(fill = "salmon", col = "black", binwidth = 0.05) + scale_x_log10()+theme_classic()dat %>%
summarise( # Calculate the number of effect sizes, studies and species for the main categorical variables
`Studies` = n_distinct(Study_ID),
`Species` = n_distinct(Species_common),
`PFAS type` = n_distinct(PFAS_type),
`Cohorts` = n_distinct(Cohort_ID),
`Effect sizes` = n_distinct(Effect_ID),
`Effect sizes (Oil-based)` = n_distinct(Effect_ID[Cooking_Category=="oil-based"]),
`Studies (Oil-based)` = n_distinct(Study_ID[Cooking_Category=="oil-based"]),
`Species (Oil-based)` = n_distinct(Species_common[Cooking_Category=="oil-based"]),
`Effect sizes (Water-based)` = n_distinct(Effect_ID[Cooking_Category=="water-based"]),
`Studies (Water-based)` = n_distinct(Study_ID[Cooking_Category=="water-based"]),
`Species (Water-based)` = n_distinct(Species_common[Cooking_Category=="water-based"]),
`Effect sizes (No liquid)` = n_distinct(Effect_ID[Cooking_Category=="No liquid"]),
`Studies (No liquid)` = n_distinct(Study_ID[Cooking_Category=="No liquid"]),
`Species (No liquid)` = n_distinct(Species_common[Cooking_Category=="No liquid"]),) -> table_sample_sizes
table_sample_sizes<-t(table_sample_sizes)
colnames(table_sample_sizes)<-"n (sample size)"
kable(table_sample_sizes) %>% kable_styling("striped", position="left")| n (sample size) | |
|---|---|
| Studies | 10 |
| Species | 39 |
| PFAS type | 18 |
| Cohorts | 153 |
| Effect sizes | 512 |
| Effect sizes (Oil-based) | 303 |
| Studies (Oil-based) | 7 |
| Species (Oil-based) | 28 |
| Effect sizes (Water-based) | 140 |
| Studies (Water-based) | 8 |
| Species (Water-based) | 23 |
| Effect sizes (No liquid) | 69 |
| Studies (No liquid) | 2 |
| Species (No liquid) | 14 |
kable(summary(dat), "html") %>% kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")| Study_ID | Author_year | Publication_year | Country_firstAuthor | Effect_ID | Species_common | Species_Scientific | Invertebrate_vertebrate | Fish_mollusc | Moisture_loss_in_percent | PFAS_type | PFAS_carbon_chain | linear_total | Choice_of_9 | Cooking_method | Cooking_Category | Comments_cooking | Temperature_in_Celsius | Length_cooking_time_in_s | Water | Oil | Oil_type | Volume_liquid_ml | Volume_liquid_ml_0 | Ratio_liquid_fish | Weigh_g_sample | Cohort_ID | Cohort_comment | Nc | Pooled_Nc | Unit_PFAS_conc | Mc | Mc_comment | Sc | sd | Sc_technical_biological | Ne | Pooled_Ne | Me | Me_comment | Se | Se_technical_biological | If_technical_how_many | Unit_LOD_LOQ | LOD | LOQ | Design | DataSource | Raw_data_provided | General_comments | checked | SDc | SDe | Phylogeny | N_tilde | lnRR | var_lnRR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Length:512 | Length:512 | Min. :2008 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Min. : 6.77 | Length:512 | Min. : 3.000 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Min. : 75.0 | Min. : 120.0 | Length:512 | Length:512 | Length:512 | Min. : 0.341 | Min. : 0.0 | Min. : 0.00266 | Min. : 10.0 | Length:512 | Length:512 | Min. : 1.00 | Min. :1.000 | Length:512 | Min. : 0.002 | Length:512 | Min. : 0.0010 | Length:512 | Length:512 | Min. : 1.00 | Min. :1.000 | Min. : 0.0020 | Length:512 | Min. : 0.000 | Length:512 | Min. :1.000 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Min. : 0.0010 | Min. : 0.0010 | Length:512 | Min. : 0.500 | Min. :-6.0350 | Min. :-0.02482 | |
| Class :character | Class :character | 1st Qu.:2014 | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | 1st Qu.:14.45 | Class :character | 1st Qu.: 8.000 | Class :character | Class :character | Class :character | Class :character | Class :character | 1st Qu.:100.0 | 1st Qu.: 600.0 | Class :character | Class :character | Class :character | 1st Qu.: 11.000 | 1st Qu.: 5.0 | 1st Qu.: 0.10004 | 1st Qu.: 10.0 | Class :character | Class :character | 1st Qu.: 5.00 | 1st Qu.:1.000 | Class :character | 1st Qu.: 0.160 | Class :character | 1st Qu.: 0.0010 | Class :character | Class :character | 1st Qu.: 5.00 | 1st Qu.:1.000 | 1st Qu.: 0.0940 | Class :character | 1st Qu.: 0.001 | Class :character | 1st Qu.:1.000 | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | 1st Qu.: 0.0354 | 1st Qu.: 0.0585 | Class :character | 1st Qu.: 2.500 | 1st Qu.:-0.8778 | 1st Qu.: 0.02398 | |
| Mode :character | Mode :character | Median :2019 | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Median :18.35 | Mode :character | Median : 8.000 | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Median :160.0 | Median : 600.0 | Mode :character | Mode :character | Mode :character | Median : 300.000 | Median : 250.0 | Median : 2.50000 | Median : 70.0 | Mode :character | Mode :character | Median :10.00 | Median :1.000 | Mode :character | Median : 0.298 | Mode :character | Median : 0.0100 | Mode :character | Mode :character | Median :10.00 | Median :1.000 | Median : 0.2285 | Mode :character | Median : 0.020 | Mode :character | Median :3.000 | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Median : 0.1580 | Median : 0.1461 | Mode :character | Median : 5.000 | Median :-0.1671 | Median : 0.02398 | |
| NA | NA | Mean :2017 | NA | NA | NA | NA | NA | NA | Mean :21.04 | NA | Mean : 8.994 | NA | NA | NA | NA | NA | Mean :161.3 | Mean : 733.3 | NA | NA | NA | Mean : 271.946 | Mean : 231.8 | Mean :13.58240 | Mean : 149.1 | NA | NA | Mean :10.34 | Mean :2.316 | NA | Mean : 3.494 | NA | Mean : 1.7676 | NA | NA | Mean :11.49 | Mean :2.371 | Mean : 3.2321 | NA | Mean : 1.822 | NA | Mean :2.481 | NA | NA | NA | NA | NA | NA | NA | NA | Mean : 4.4069 | Mean : 4.4491 | NA | Mean : 5.297 | Mean :-0.3637 | Mean : 0.03283 | |
| NA | NA | 3rd Qu.:2019 | NA | NA | NA | NA | NA | NA | 3rd Qu.:21.31 | NA | 3rd Qu.:11.000 | NA | NA | NA | NA | NA | 3rd Qu.:175.0 | 3rd Qu.: 900.0 | NA | NA | NA | 3rd Qu.: 300.000 | 3rd Qu.: 300.0 | 3rd Qu.:30.00000 | 3rd Qu.: 178.4 | NA | NA | 3rd Qu.:10.00 | 3rd Qu.:5.000 | NA | 3rd Qu.: 1.083 | NA | 3rd Qu.: 0.1185 | NA | NA | 3rd Qu.:10.00 | 3rd Qu.:5.000 | 3rd Qu.: 1.0505 | NA | 3rd Qu.: 0.130 | NA | 3rd Qu.:3.000 | NA | NA | NA | NA | NA | NA | NA | NA | 3rd Qu.: 0.5600 | 3rd Qu.: 0.6516 | NA | 3rd Qu.: 5.000 | 3rd Qu.: 0.1849 | 3rd Qu.: 0.04797 | |
| NA | NA | Max. :2020 | NA | NA | NA | NA | NA | NA | Max. :79.11 | NA | Max. :14.000 | NA | NA | NA | NA | NA | Max. :300.0 | Max. :1500.0 | NA | NA | NA | Max. :2500.000 | Max. :2500.0 | Max. :45.33092 | Max. :1000.0 | NA | NA | Max. :50.00 | Max. :6.000 | NA | Max. :86.689 | NA | Max. :133.7000 | NA | NA | Max. :60.00 | Max. :6.000 | Max. :134.4379 | NA | Max. :130.500 | NA | Max. :4.000 | NA | NA | NA | NA | NA | NA | NA | NA | Max. :133.7000 | Max. :130.5000 | NA | Max. :25.000 | Max. : 3.4622 | Max. : 0.23983 | |
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA’s :284 | NA | NA | NA | NA | NA | NA | NA | NA’s :6 | NA’s :56 | NA | NA | NA | NA’s :114 | NA’s :45 | NA’s :88 | NA’s :106 | NA | NA | NA | NA | NA | NA | NA | NA’s :53 | NA | NA | NA | NA | NA | NA | NA’s :55 | NA | NA’s :198 | NA | NA | NA | NA | NA | NA | NA | NA | NA’s :330 | NA’s :328 | NA | NA | NA | NA |
Cohort_ID explains virtually no variance in the model. Hence, it was removed from the model. All the other random effects explained significant variance and were kept in subsequent models
MA_all_rand_effects <- rma.mv(lnRR, VCV_lnRR, # Add `VCV_lnRR` to account for correlated errors errors between cohorts (shared_controls)
random = list(~1|Study_ID, # Identity of the study
~1|Phylogeny, # Phylogenetic correlation
~1|Cohort_ID, # Identity of the cohort (shared controls)
~1|Species_common, # Non-phylogenetic correlation between species
~1|PFAS_type, # Type of PFAS
~1|Effect_ID), # Effect size identity
R= list(Phylogeny = cor_tree), # Assign the 'Phylogeny' argument to the phylogenetic variance-covariance matrix
test = "t",
data = dat,
sparse = TRUE)
summary(MA_all_rand_effects) # Cohort ID does not explain any variance ##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -625.3701 1250.7402 1264.7402 1294.3947 1264.9628
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5837 0.7640 10 no Study_ID no
## sigma^2.2 0.0000 0.0005 38 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 153 no Cohort_ID no
## sigma^2.4 0.2222 0.4714 39 no Species_common no
## sigma^2.5 0.0973 0.3119 18 no PFAS_type no
## sigma^2.6 0.5003 0.7073 512 no Effect_ID no
##
## Test for Heterogeneity:
## Q(df = 511) = 11056.9620, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## -0.3259 0.2856 -1.1413 511 0.2543 -0.8871 0.2352
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
MA_model <- rma.mv(lnRR, VCV_lnRR,
random = list(~1|Study_ID,
~1|Phylogeny, # Removed Cohort_ID
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
R= list(Phylogeny = cor_tree),
test = "t",
data = dat,
sparse = TRUE)
summary(MA_model)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -625.3701 1250.7402 1262.7402 1288.1584 1262.9068
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5837 0.7640 10 no Study_ID no
## sigma^2.2 0.0000 0.0006 38 no Phylogeny yes
## sigma^2.3 0.2222 0.4714 39 no Species_common no
## sigma^2.4 0.0973 0.3119 18 no PFAS_type no
## sigma^2.5 0.5003 0.7073 512 no Effect_ID no
##
## Test for Heterogeneity:
## Q(df = 511) = 11056.9620, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## -0.3259 0.2856 -1.1413 511 0.2543 -0.8871 0.2352
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
round(i2_ml(MA_model)*100,2) # Percentage of heterogeneity explained by each random effect## I2_total I2_Study_ID I2_Phylogeny I2_Species_common
## 94.62 39.35 0.00 14.98
## I2_PFAS_type I2_Effect_ID
## 6.56 33.73
# plot
orchard_plot(MA_model, mod = "Int", xlab = "lnRR", alpha=0.4) + # Orchard plot
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5)+ # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2)+ # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_colour_manual(values = "darkorange")+ # change colours
scale_fill_manual(values="darkorange")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) save(MA_model, MA_all_rand_effects, file = here("Rdata", "int_MA_models.RData")) # save the models run_model<-function(data,formula){
data<-as.data.frame(data) # convert data set into a data frame to calculate VCV matrix
VCV<-make_VCV_matrix(data
, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
rma.mv(lnRR, VCV, # run the model, as described earlier
mods=formula,
random = list(~1|Study_ID,
~1|Phylogeny,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
R= list(Phylogeny = cor_tree),
test = "t",
data = data,
sparse=TRUE) # Make the model run faster
}plot_continuous<-function(data, model, moderator, xlab){
pred<-predict.rma(model)
data %>% mutate(fit=pred$pred,
ci.lb=pred$ci.lb,
ci.ub=pred$ci.ub,
pr.lb=pred$cr.lb,
pr.ub=pred$cr.ub) %>% # Add confidence intervals, mean predictions and prediction intervals
ggplot(aes(x = moderator, y = lnRR)) +
geom_ribbon(aes(ymin = pr.lb, ymax = pr.ub, color = NULL), alpha = .075) + # Shaded area for prediction intervals
geom_ribbon(aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = .2) + # Shaded area for confidence intervals
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) + # Points scaled by precision
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
geom_line(aes(y = fit), size = 1.5)+ # Regression line
labs(x = xlab, y = "lnRR", size = "Precison (1/SE)") +
theme_bw() +
scale_size_continuous(range=c(1,9))+ # Point scaling
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))
}All continuous variables were z-transformed
# Length_cooking_time_in_s
time_model<-run_model(dat, ~scale(Length_cooking_time_in_s)) # z-transformed
summary(time_model)##
## Multivariate Meta-Analysis Model (k = 456; method: REML)
##
## logLik Deviance AIC BIC AICc
## -504.8213 1009.6425 1023.6425 1052.4692 1023.8937
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5265 0.7256 9 no Study_ID no
## sigma^2.2 0.0000 0.0002 30 no Phylogeny yes
## sigma^2.3 0.2024 0.4499 30 no Species_common no
## sigma^2.4 0.0933 0.3055 17 no PFAS_type no
## sigma^2.5 0.4269 0.6534 456 no Effect_ID no
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 454) = 29.6432, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.5450 0.2866 -1.9020 454 0.0578
## scale(Length_cooking_time_in_s) -0.2369 0.0435 -5.4446 454 <.0001
## ci.lb ci.ub
## intrcpt -1.1082 0.0181 .
## scale(Length_cooking_time_in_s) -0.3224 -0.1514 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(time_model) # Estimate R squared## R2_marginal R2_coditional
## 0.04299422 0.67291392
# Plot
dat.time<-filter(dat, Length_cooking_time_in_s!="NA") # Need to remove the NAs from the data
plot_continuous(dat.time, time_model, dat.time$Length_cooking_time_in_s, "Cooking time (s)")# Ratio_liquid_fish
volume_model<-run_model(dat, ~scale(log(Ratio_liquid_fish))) # logged and z-transformed
summary(volume_model)##
## Multivariate Meta-Analysis Model (k = 424; method: REML)
##
## logLik Deviance AIC BIC AICc
## -523.0709 1046.1418 1060.1418 1088.4568 1060.4123
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5693 0.7546 8 no Study_ID no
## sigma^2.2 0.0000 0.0001 34 no Phylogeny yes
## sigma^2.3 0.1914 0.4375 35 no Species_common no
## sigma^2.4 0.1086 0.3295 18 no PFAS_type no
## sigma^2.5 0.5598 0.7482 424 no Effect_ID no
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 422) = 3.1947, p-val = 0.0746
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -0.4401 0.3041 -1.4469 422 0.1487 -1.0379
## scale(log(Ratio_liquid_fish)) -0.1967 0.1100 -1.7874 422 0.0746 -0.4130
## ci.ub
## intrcpt 0.1578
## scale(log(Ratio_liquid_fish)) 0.0196 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(volume_model)## R2_marginal R2_coditional
## 0.0263583 0.6185907
# Plot
dat.volume<-filter(dat, Ratio_liquid_fish!="NA")
plot_continuous(dat.volume, volume_model, log(dat.volume$Ratio_liquid_fish), "ln (Tissue to liquid volume ratio)")# Temperature_in_Celsius
temp_model<-run_model(dat, ~scale(Temperature_in_Celsius)) # z-transformed
summary(temp_model)##
## Multivariate Meta-Analysis Model (k = 506; method: REML)
##
## logLik Deviance AIC BIC AICc
## -606.6607 1213.3215 1227.3215 1256.8795 1227.5473
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5760 0.7590 10 no Study_ID no
## sigma^2.2 0.0000 0.0002 38 no Phylogeny yes
## sigma^2.3 0.2417 0.4916 39 no Species_common no
## sigma^2.4 0.0903 0.3005 18 no PFAS_type no
## sigma^2.5 0.5177 0.7195 506 no Effect_ID no
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 504) = 0.0403, p-val = 0.8410
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -0.3084 0.2845 -1.0839 504 0.2789 -0.8674
## scale(Temperature_in_Celsius) 0.0118 0.0589 0.2008 504 0.8410 -0.1039
## ci.ub
## intrcpt 0.2506
## scale(Temperature_in_Celsius) 0.1275
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(temp_model)## R2_marginal R2_coditional
## 9.801312e-05 6.368936e-01
# Plot
dat.temp<-filter(dat, Temperature_in_Celsius!="NA")
plot_continuous(dat.temp, temp_model, dat.temp$Temperature_in_Celsius, "Cooking temperature")# PFAS_carbon_chain
PFAS_model<-run_model(dat, ~PFAS_carbon_chain)
summary(PFAS_model)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -613.5559 1227.1117 1241.1117 1270.7526 1241.3349
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5812 0.7624 10 no Study_ID no
## sigma^2.2 0.0000 0.0002 38 no Phylogeny yes
## sigma^2.3 0.2455 0.4955 39 no Species_common no
## sigma^2.4 0.0975 0.3123 18 no PFAS_type no
## sigma^2.5 0.5168 0.7189 512 no Effect_ID no
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 510) = 0.2339, p-val = 0.6289
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.4557 0.3945 -1.1552 510 0.2485 -1.2307 0.3193
## PFAS_carbon_chain 0.0145 0.0301 0.4836 510 0.6289 -0.0445 0.0736
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(PFAS_model)## R2_marginal R2_coditional
## 0.0007957533 0.6416647969
plot_continuous(dat, PFAS_model, dat$PFAS_carbon_chain, "PFAS carbon chain length")# Cooking_Category
category_model<-run_model(dat, ~Cooking_Category-1)
summary(category_model)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -611.8868 1223.7736 1239.7736 1273.6332 1240.0616
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5868 0.7660 10 no Study_ID no
## sigma^2.2 0.0000 0.0002 38 no Phylogeny yes
## sigma^2.3 0.2468 0.4968 39 no Species_common no
## sigma^2.4 0.0957 0.3094 18 no PFAS_type no
## sigma^2.5 0.5150 0.7176 512 no Effect_ID no
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 509) = 1.4090, p-val = 0.2393
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Cooking_CategoryNo liquid -0.2047 0.3048 -0.6716 509 0.5021 -0.8036
## Cooking_Categoryoil-based -0.3865 0.2918 -1.3247 509 0.1859 -0.9597
## Cooking_Categorywater-based -0.2898 0.2902 -0.9985 509 0.3185 -0.8600
## ci.ub
## Cooking_CategoryNo liquid 0.3941
## Cooking_Categoryoil-based 0.1867
## Cooking_Categorywater-based 0.2804
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(category_model)## R2_marginal R2_coditional
## 0.003053805 0.644527289
# plot
orchard_plot(category_model, mod = "Cooking_Category", xlab = "lnRR", alpha=0.4)+
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5)+ # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = FALSE, size = 2)+ # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_colour_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3"))+ # change colours
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))This analysis is a posteriori and will only be presented in supplement.
# Moisture_loss_in_percent
moisture_model<-run_model(dat, ~scale(Moisture_loss_in_percent))
summary(moisture_model)##
## Multivariate Meta-Analysis Model (k = 228; method: REML)
##
## logLik Deviance AIC BIC AICc
## -212.7263 425.4525 439.4525 463.3963 439.9663
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.0836 0.2891 6 no Study_ID no
## sigma^2.2 0.2283 0.4778 18 no Phylogeny yes
## sigma^2.3 0.1395 0.3736 18 no Species_common no
## sigma^2.4 0.0110 0.1047 17 no PFAS_type no
## sigma^2.5 0.3090 0.5559 228 no Effect_ID no
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 226) = 0.1639, p-val = 0.6859
##
## Model Results:
##
## estimate se tval df pval
## intrcpt 0.5304 0.3288 1.6134 226 0.1081
## scale(Moisture_loss_in_percent) -0.0244 0.0604 -0.4049 226 0.6859
## ci.lb ci.ub
## intrcpt -0.1174 1.1783
## scale(Moisture_loss_in_percent) -0.1434 0.0945
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(moisture_model)## R2_marginal R2_coditional
## 0.0007742055 0.5997281245
# Plot
dat.moisture<-filter(dat, Moisture_loss_in_percent!="NA")
plot_continuous(dat.moisture, moisture_model, dat.moisture$Moisture_loss_in_percent, "Percentage of moisture loss")save(category_model, PFAS_model, temp_model, time_model, volume_model, moisture_model, file = here("Rdata", "single_mod_models.RData")) # Save models# Full_model
full_model <- run_model(dat, ~ - 1 + # All predictors, with individual coefficients for each cooking category
Cooking_Category +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(full_model)##
## Multivariate Meta-Analysis Model (k = 384; method: REML)
##
## logLik Deviance AIC BIC AICc
## -420.4347 840.8695 862.8695 906.1533 863.5908
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.4150 0.6442 7 no Study_ID no
## sigma^2.2 0.4296 0.6554 26 no Phylogeny yes
## sigma^2.3 0.0415 0.2037 26 no Species_common no
## sigma^2.4 0.1145 0.3383 17 no PFAS_type no
## sigma^2.5 0.4270 0.6535 384 no Effect_ID no
##
## Test of Moderators (coefficients 1:6):
## F(df1 = 6, df2 = 378) = 8.6502, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## Cooking_Categoryoil-based -0.6517 0.4473 -1.4570 378 0.1460
## Cooking_Categorywater-based -0.7815 0.4468 -1.7492 378 0.0811
## scale(Temperature_in_Celsius) -0.3306 0.1104 -2.9941 378 0.0029
## scale(Length_cooking_time_in_s) -0.3043 0.0497 -6.1250 378 <.0001
## scale(PFAS_carbon_chain) 0.0669 0.0770 0.8686 378 0.3856
## scale(log(Ratio_liquid_fish)) -0.7663 0.1739 -4.4056 378 <.0001
## ci.lb ci.ub
## Cooking_Categoryoil-based -1.5313 0.2278
## Cooking_Categorywater-based -1.6599 0.0970 .
## scale(Temperature_in_Celsius) -0.5477 -0.1135 **
## scale(Length_cooking_time_in_s) -0.4019 -0.2066 ***
## scale(PFAS_carbon_chain) -0.0845 0.2183
## scale(log(Ratio_liquid_fish)) -1.1083 -0.4243 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model)## R2_marginal R2_coditional
## 0.2705281 0.7817900
full_modelb <- run_model(dat, ~ 1 + # All predictors, with oil-based as the reference cooking category
relevel(factor(Cooking_Category),ref = "oil-based") +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(full_modelb)##
## Multivariate Meta-Analysis Model (k = 384; method: REML)
##
## logLik Deviance AIC BIC AICc
## -420.4347 840.8695 862.8695 906.1533 863.5908
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.4150 0.6442 7 no Study_ID no
## sigma^2.2 0.4296 0.6554 26 no Phylogeny yes
## sigma^2.3 0.0415 0.2037 26 no Species_common no
## sigma^2.4 0.1145 0.3383 17 no PFAS_type no
## sigma^2.5 0.4270 0.6535 384 no Effect_ID no
##
## Test of Moderators (coefficients 2:6):
## F(df1 = 5, df2 = 378) = 9.9573, p-val < .0001
##
## Model Results:
##
## estimate
## intrcpt -0.6517
## relevel(factor(Cooking_Category), ref = "oil-based")water-based -0.1297
## scale(Temperature_in_Celsius) -0.3306
## scale(Length_cooking_time_in_s) -0.3043
## scale(PFAS_carbon_chain) 0.0669
## scale(log(Ratio_liquid_fish)) -0.7663
## se
## intrcpt 0.4473
## relevel(factor(Cooking_Category), ref = "oil-based")water-based 0.1471
## scale(Temperature_in_Celsius) 0.1104
## scale(Length_cooking_time_in_s) 0.0497
## scale(PFAS_carbon_chain) 0.0770
## scale(log(Ratio_liquid_fish)) 0.1739
## tval df
## intrcpt -1.4570 378
## relevel(factor(Cooking_Category), ref = "oil-based")water-based -0.8817 378
## scale(Temperature_in_Celsius) -2.9941 378
## scale(Length_cooking_time_in_s) -6.1250 378
## scale(PFAS_carbon_chain) 0.8686 378
## scale(log(Ratio_liquid_fish)) -4.4056 378
## pval
## intrcpt 0.1460
## relevel(factor(Cooking_Category), ref = "oil-based")water-based 0.3785
## scale(Temperature_in_Celsius) 0.0029
## scale(Length_cooking_time_in_s) <.0001
## scale(PFAS_carbon_chain) 0.3856
## scale(log(Ratio_liquid_fish)) <.0001
## ci.lb
## intrcpt -1.5313
## relevel(factor(Cooking_Category), ref = "oil-based")water-based -0.4191
## scale(Temperature_in_Celsius) -0.5477
## scale(Length_cooking_time_in_s) -0.4019
## scale(PFAS_carbon_chain) -0.0845
## scale(log(Ratio_liquid_fish)) -1.1083
## ci.ub
## intrcpt 0.2278
## relevel(factor(Cooking_Category), ref = "oil-based")water-based 0.1596
## scale(Temperature_in_Celsius) -0.1135 **
## scale(Length_cooking_time_in_s) -0.2066 ***
## scale(PFAS_carbon_chain) 0.2183
## scale(log(Ratio_liquid_fish)) -0.4243 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
full_modelc <- run_model(dat, ~ 1 + # All predictors, with No liquid as the reference cooking category
relevel(factor(Cooking_Category),ref = "No liquid") +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(full_modelc)##
## Multivariate Meta-Analysis Model (k = 384; method: REML)
##
## logLik Deviance AIC BIC AICc
## -420.4347 840.8695 862.8695 906.1533 863.5908
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.4150 0.6442 7 no Study_ID no
## sigma^2.2 0.4296 0.6554 26 no Phylogeny yes
## sigma^2.3 0.0415 0.2037 26 no Species_common no
## sigma^2.4 0.1145 0.3383 17 no PFAS_type no
## sigma^2.5 0.4270 0.6535 384 no Effect_ID no
##
## Test of Moderators (coefficients 2:6):
## F(df1 = 5, df2 = 378) = 9.9573, p-val < .0001
##
## Model Results:
##
## estimate se
## intrcpt -0.7815 0.4468
## relevel(factor(Cooking_Category), ref = "No liquid")oil-based 0.1297 0.1471
## scale(Temperature_in_Celsius) -0.3306 0.1104
## scale(Length_cooking_time_in_s) -0.3043 0.0497
## scale(PFAS_carbon_chain) 0.0669 0.0770
## scale(log(Ratio_liquid_fish)) -0.7663 0.1739
## tval df
## intrcpt -1.7492 378
## relevel(factor(Cooking_Category), ref = "No liquid")oil-based 0.8817 378
## scale(Temperature_in_Celsius) -2.9941 378
## scale(Length_cooking_time_in_s) -6.1250 378
## scale(PFAS_carbon_chain) 0.8686 378
## scale(log(Ratio_liquid_fish)) -4.4056 378
## pval ci.lb
## intrcpt 0.0811 -1.6599
## relevel(factor(Cooking_Category), ref = "No liquid")oil-based 0.3785 -0.1596
## scale(Temperature_in_Celsius) 0.0029 -0.5477
## scale(Length_cooking_time_in_s) <.0001 -0.4019
## scale(PFAS_carbon_chain) 0.3856 -0.0845
## scale(log(Ratio_liquid_fish)) <.0001 -1.1083
## ci.ub
## intrcpt 0.0970 .
## relevel(factor(Cooking_Category), ref = "No liquid")oil-based 0.4191
## scale(Temperature_in_Celsius) -0.1135 **
## scale(Length_cooking_time_in_s) -0.2066 ***
## scale(PFAS_carbon_chain) 0.2183
## scale(log(Ratio_liquid_fish)) -0.4243 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Testing cooking categories
full_modeld <- rma.mv(yi = lnRR, V = VCV_lnRR,
mods= ~-1 +
Cooking_Category +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)),
random = list(~1|Study_ID,
~1|Phylogeny,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
R= list(Phylogeny = cor_tree),
test = "t",
data = dat,
btt = c(1:3)) # testing the significance of cooking category - testing first 3 regression coefficients)
summary(full_modeld)##
## Multivariate Meta-Analysis Model (k = 384; method: REML)
##
## logLik Deviance AIC BIC AICc
## -420.4347 840.8695 862.8695 906.1533 863.5908
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.4150 0.6442 7 no Study_ID no
## sigma^2.2 0.4296 0.6554 26 no Phylogeny yes
## sigma^2.3 0.0415 0.2037 26 no Species_common no
## sigma^2.4 0.1145 0.3383 17 no PFAS_type no
## sigma^2.5 0.4270 0.6535 384 no Effect_ID no
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 378) = 4.7207, p-val = 0.0030
##
## Model Results:
##
## estimate se tval df pval
## Cooking_Categoryoil-based -0.6517 0.4473 -1.4570 378 0.1460
## Cooking_Categorywater-based -0.7815 0.4468 -1.7492 378 0.0811
## scale(Temperature_in_Celsius) -0.3306 0.1104 -2.9941 378 0.0029
## scale(Length_cooking_time_in_s) -0.3043 0.0497 -6.1250 378 <.0001
## scale(PFAS_carbon_chain) 0.0669 0.0770 0.8686 378 0.3856
## scale(log(Ratio_liquid_fish)) -0.7663 0.1739 -4.4056 378 <.0001
## ci.lb ci.ub
## Cooking_Categoryoil-based -1.5313 0.2278
## Cooking_Categorywater-based -1.6599 0.0970 .
## scale(Temperature_in_Celsius) -0.5477 -0.1135 **
## scale(Length_cooking_time_in_s) -0.4019 -0.2066 ***
## scale(PFAS_carbon_chain) -0.0845 0.2183
## scale(log(Ratio_liquid_fish)) -1.1083 -0.4243 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
save(full_model,full_modelb,full_modelc,full_modeld, file = here("Rdata", "full_model.RData"))## Check for collinerarity - seems fine
vif(full_model)##
## Cooking_Categoryoil-based Cooking_Categorywater-based
## 18.7469 18.9773
## scale(Temperature_in_Celsius) scale(Length_cooking_time_in_s)
## 3.7908 1.0533
## scale(PFAS_carbon_chain) scale(log(Ratio_liquid_fish))
## 1.0002 2.4217
dat %>% select(Temperature_in_Celsius, Length_cooking_time_in_s, PFAS_carbon_chain, Ratio_liquid_fish) %>%
ggpairs() # Estimate correlations between the variablesInspection of the plots highlighted potential significant decreases in PFAS content with increased cooking time and volume of cooking. Hence, here we used emmeans (download from remotes::install_github(“rvlenth/emmeans”, dependencies = TRUE, build_opts = "")) to generate marginalised means at specified values of the different predictors. Such analysis enable the quantification of the mean effect size after controlling for different values of the moderators.
# Full model in original units (no z-transformation)
dat$log_Ratio_liquid_fish<-log(dat$Ratio_liquid_fish)
full_model_org_units <- run_model(dat, ~ - 1 +
Cooking_Category +
Temperature_in_Celsius +
Length_cooking_time_in_s +
PFAS_carbon_chain +
log_Ratio_liquid_fish)
# Full model in original units (no z-transformation), but without the "No liquid" data
# This model will be used for conditional analyses on the volume of liquid, where the data without liquid is irrelevant.
dat_oil_water<-filter(dat, Cooking_Category!="No liquid")
full_model_org_units_oil_water<- run_model(dat_oil_water, ~ - 1 +
Cooking_Category +
Temperature_in_Celsius +
Length_cooking_time_in_s +
PFAS_carbon_chain +
log_Ratio_liquid_fish)
save(full_model_org_units,full_model_org_units_oil_water, file = here("Rdata", "full_models_org_units.RData"))Overall mean at the mean of each other predictor
res<-marginal_means(model = full_model_org_units, data=dat, mod="1")
res$mod_table## name estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt -0.741503 -1.60787 0.1248641 -3.245475 1.762469
Overall mean for each cooking category, at the mean of each other predictor
res_cat<-marginal_means(full_model_org_units, data = dat, mod = "1", by = "Cooking_Category")
res_cat$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt oil-based -0.6766372 -1.554017 0.20074288 -3.184441 1.831166
## 2 Intrcpt water-based -0.8063688 -1.685711 0.07297302 -3.314859 1.702122
orchard_plot(res_cat, xlab = "lnRR", condition.lab = "Cooking Category" )Here, we generate estimates at cooking times of 2, 10, and 25 min.
res_cooking_time <-marginal_means(full_model_org_units, data = dat, mod = "1", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
res_cooking_time$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 120 -0.1168591 -0.994837 0.7611188 -2.624872 2.3911535
## 2 Intrcpt 600 -0.6165742 -1.481565 0.2484166 -3.120070 1.8869215
## 3 Intrcpt 1500 -1.5535400 -2.472502 -0.6345781 -4.076192 0.9691119
orchard_plot(res_cooking_time, xlab = "lnRR", condition.lab = "Cooking time (sec)")Marginalised means for each cooking category, at different cooking times
res_cooking_time_cat <-marginal_means(full_model_org_units, data = dat, mod = "Cooking_Category", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
res_cooking_time_cat$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Oil-based 120 -0.05199328 -0.9438408 0.8398542 -2.564895 2.4609082
## 2 Water-based 120 -0.18172492 -1.0695044 0.7060546 -2.693186 2.3297357
## 3 Oil-based 600 -0.55170839 -1.4283391 0.3249223 -3.059250 1.9558329
## 4 Water-based 600 -0.68144003 -1.5588171 0.1959370 -3.189242 1.8263623
## 5 Oil-based 1500 -1.48867422 -2.4142816 -0.5630668 -4.013755 1.0364061
## 6 Water-based 1500 -1.61840587 -2.5533320 -0.6834798 -4.146917 0.9101053
orchard_plot(res_cooking_time_cat, xlab = "lnRR", condition.lab = "Cooking time (sec)")Here, we generate marginalised estimates at volumes of liquid of ~0.1mL/g of tissue, ~10 ml/g of tissue, or 45 mL/g of tissue. We did not look at the means for different cooking categories because they are inherently different in the volume of liquid used. We also only used the data on oil and water because the “No liquid” category is not relevant for this analysis.
res_volume<-marginal_means(full_model_org_units_oil_water, data = dat_oil_water, mod = "1", at = list(log_Ratio_liquid_fish = c(log(0.1), log(10), log(45))), by = "log_Ratio_liquid_fish")
res_volume$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt -2.302585 -0.08560513 -1.005423 0.8342126 -2.608569 2.4373587
## 2 Intrcpt 2.302585 -1.18919796 -2.074546 -0.3038500 -3.699800 1.3214041
## 3 Intrcpt 3.806662 -1.54963829 -2.481642 -0.6176342 -4.077070 0.9777939
orchard_plot(res_volume, xlab = "lnRR", condition.lab = "ln(Liquid volume to tissue sample ratio (mL/g))")Here, we generate marginalized estimates for PFAS of 3, 6, and 12 carbon chains
res_PFAS<-marginal_means(full_model_org_units, data = dat, mod = "1", at = list(PFAS_carbon_chain= c(3, 6, 12)), by = "PFAS_carbon_chain")
res_PFAS$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 3 -0.9154879 -1.869803 0.03882708 -3.451232 1.620256
## 2 Intrcpt 6 -0.8293923 -1.719733 0.06094887 -3.341760 1.682975
## 3 Intrcpt 12 -0.6572010 -1.542964 0.22856173 -3.167949 1.853547
orchard_plot(res_PFAS, xlab = "lnRR", condition.lab = "PFAS carbon chain")Marginalised mean estimate for each PFAS carbon chain, for each cooking category
res_PFAS_cat<-marginal_means(full_model_org_units, data = dat, mod = "Cooking_Category", at = list(PFAS_carbon_chain= c(3, 6, 12)), by = "PFAS_carbon_chain")
res_PFAS_cat$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Oil-based 3 -0.8506221 -1.815047 0.113802623 -3.390188 1.688944
## 2 Water-based 3 -0.9803537 -1.946362 -0.014344914 -3.520522 1.559815
## 3 Oil-based 6 -0.7645264 -1.665642 0.136589073 -3.280732 1.751679
## 4 Water-based 6 -0.8942581 -1.797175 0.008659035 -3.411110 1.622593
## 5 Oil-based 12 -0.5923352 -1.488820 0.304149803 -3.106886 1.922216
## 6 Water-based 12 -0.7220669 -1.620577 0.176442926 -3.237341 1.793207
orchard_plot(res_PFAS_cat, xlab = "lnRR", condition.lab = "PFAS carbon chain") ##
Here, we investigated whether the effect of the continuous moderators on lnRR vary depending on the cooking category. Hence, we performed subset analyses for each cooking category.
oil_dat<-filter(dat, Cooking_Category=="oil-based")
include <- row.names(cor_tree) %in% oil_dat$Phylogeny # Check which rows are present in the phylogenetic tree
cor_tree_oil <- cor_tree[include, include] # Only include the species that match the reduced data set
run_model_oil<-function(data,formula){
data<-as.data.frame(data) # convert data set into a data frame to calculate VCV matrix
VCV<-make_VCV_matrix(data, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
rma.mv(lnRR, VCV, # run the model, as described earlier
mods=formula,
random = list(~1|Study_ID,
~1|Phylogeny,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
R= list(Phylogeny = cor_tree_oil), # cor_tree_oil here
test = "t",
data = data)
}full_model_oil <- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(full_model_oil)##
## Multivariate Meta-Analysis Model (k = 263; method: REML)
##
## logLik Deviance AIC BIC AICc
## -164.7225 329.4451 349.4451 384.9747 350.3357
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1644 0.4054 6 no Study_ID no
## sigma^2.2 0.0000 0.0000 19 no Phylogeny yes
## sigma^2.3 0.0170 0.1304 19 no Species_common no
## sigma^2.4 0.0546 0.2336 16 no PFAS_type no
## sigma^2.5 0.1543 0.3929 263 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 258) = 3515.5548, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 258) = 21.5926, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.5687 0.1958 -2.9040 258 0.0040
## scale(Temperature_in_Celsius) -0.0581 0.1038 -0.5596 258 0.5762
## scale(Length_cooking_time_in_s) -0.3536 0.0392 -9.0207 258 <.0001
## scale(PFAS_carbon_chain) 0.1264 0.0614 2.0571 258 0.0407
## scale(log(Ratio_liquid_fish)) -0.1119 0.2055 -0.5445 258 0.5866
## ci.lb ci.ub
## intrcpt -0.9543 -0.1831 **
## scale(Temperature_in_Celsius) -0.2624 0.1463
## scale(Length_cooking_time_in_s) -0.4308 -0.2764 ***
## scale(PFAS_carbon_chain) 0.0054 0.2474 *
## scale(log(Ratio_liquid_fish)) -0.5164 0.2927
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
save(full_model_oil, file = here("Rdata", "full_model_oil.RData"))water_dat<-filter(dat, Cooking_Category=="water-based")
include <- row.names(cor_tree) %in% water_dat$Phylogeny # Check which rows are present in the phylogenetic tree
cor_tree_water <- cor_tree[include, include] # Only include the species that match the reduced data set
run_model_water<-function(data,formula){
data<-as.data.frame(data) # convert data set into a data frame to calculate VCV matrix
VCV<-make_VCV_matrix(data, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
rma.mv(lnRR, VCV, # run the model, as described earlier
mods=formula,
random = list(~1|Study_ID,
~1|Phylogeny,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
R= list(Phylogeny = cor_tree_water), # cor_tree_water here
test = "t",
data = data)
}full_model_water <- run_model_water(water_dat, ~
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(full_model_water)##
## Multivariate Meta-Analysis Model (k = 121; method: REML)
##
## logLik Deviance AIC BIC AICc
## -177.4283 354.8567 372.8567 397.7162 374.5389
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5906 0.7685 6 no Study_ID no
## sigma^2.2 0.0000 0.0002 19 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 19 no Species_common no
## sigma^2.4 0.5296 0.7277 15 no PFAS_type no
## sigma^2.5 0.9622 0.9809 121 no Effect_ID no
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 117) = 4.7286, p-val = 0.0038
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -1.3257 0.4066 -3.2604 117 0.0015
## scale(Length_cooking_time_in_s) -0.3619 0.1519 -2.3819 117 0.0188
## scale(PFAS_carbon_chain) -0.0489 0.1790 -0.2732 117 0.7852
## scale(log(Ratio_liquid_fish)) -0.6500 0.2327 -2.7935 117 0.0061
## ci.lb ci.ub
## intrcpt -2.1309 -0.5204 **
## scale(Length_cooking_time_in_s) -0.6628 -0.0610 *
## scale(PFAS_carbon_chain) -0.4034 0.3056
## scale(log(Ratio_liquid_fish)) -1.1108 -0.1892 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
In our data set, the studies using steaming-based cooking were considered to have an unknown (i.e. NA) because of the difficulty to assess how much liquid gets in contact with the products. Here, we provide an analysis to compare steaming with other water-based cooking categories
water_dat$steamed<-ifelse(water_dat$Cooking_method=="Steaming","steamed","other") # create a dummy variable to differentiate "steaming" with other types of water-based cooking
full_model_water_steamed <- run_model_water(water_dat, ~ -1 + # without intercept
steamed +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain)) # In this case, we need to remove the Ratio liquid fish from the model. Otherwise, it would remove observations where the liquid volume was unknown.
summary(full_model_water_steamed)##
## Multivariate Meta-Analysis Model (k = 140; method: REML)
##
## logLik Deviance AIC BIC AICc
## -209.3770 418.7540 436.7540 462.9679 438.1825
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.6560 0.8100 8 no Study_ID no
## sigma^2.2 0.0000 0.0001 23 no Phylogeny yes
## sigma^2.3 0.0985 0.3139 23 no Species_common no
## sigma^2.4 0.2617 0.5116 15 no PFAS_type no
## sigma^2.5 0.9901 0.9950 140 no Effect_ID no
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 136) = 1.8605, p-val = 0.1209
##
## Model Results:
##
## estimate se tval df pval
## steamedother -0.7285 0.3749 -1.9433 136 0.0540
## steamedsteamed -0.5518 0.4296 -1.2844 136 0.2012
## scale(Length_cooking_time_in_s) -0.2833 0.1511 -1.8747 136 0.0630
## scale(PFAS_carbon_chain) -0.0469 0.1394 -0.3361 136 0.7373
## ci.lb ci.ub
## steamedother -1.4699 0.0128 .
## steamedsteamed -1.4015 0.2978
## scale(Length_cooking_time_in_s) -0.5822 0.0155 .
## scale(PFAS_carbon_chain) -0.3226 0.2289
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Contrast between steamed and non-steamed
full_model_water_steamed_cont <- run_model_water(water_dat,
~ steamed + # with intercept
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain))
summary(full_model_water_steamed_cont)##
## Multivariate Meta-Analysis Model (k = 140; method: REML)
##
## logLik Deviance AIC BIC AICc
## -209.3770 418.7540 436.7540 462.9679 438.1825
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.6560 0.8100 8 no Study_ID no
## sigma^2.2 0.0000 0.0001 23 no Phylogeny yes
## sigma^2.3 0.0985 0.3139 23 no Species_common no
## sigma^2.4 0.2617 0.5116 15 no PFAS_type no
## sigma^2.5 0.9901 0.9950 140 no Effect_ID no
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 136) = 1.2877, p-val = 0.2812
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.7285 0.3749 -1.9433 136 0.0540
## steamedsteamed 0.1767 0.3615 0.4888 136 0.6258
## scale(Length_cooking_time_in_s) -0.2833 0.1511 -1.8747 136 0.0630
## scale(PFAS_carbon_chain) -0.0469 0.1394 -0.3361 136 0.7373
## ci.lb ci.ub
## intrcpt -1.4699 0.0128 .
## steamedsteamed -0.5382 0.8916
## scale(Length_cooking_time_in_s) -0.5822 0.0155 .
## scale(PFAS_carbon_chain) -0.3226 0.2289
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
save(full_model_water, full_model_water_steamed, full_model_water_steamed_cont, file = here("Rdata", "full_model_water.RData"))Not very relevant because all effect sizes are from one study here. Also, the model does not converge when using VCV_lnRR
dry_dat<-filter(dat, Cooking_Category=="No liquid")
include <- row.names(cor_tree) %in% dry_dat$Phylogeny # Check which rows are present in the phylogenetic tree
cor_tree_dry <- cor_tree[include, include] # Only include the species that match the reduced data set
run_model_dry<-function(data,formula){
data<-as.data.frame(data) # convert data set into a data frame to calculate VCV matrix
rma.mv(lnRR, var_lnRR, # run the model with var_lnRR instead of VCV
mods=formula,
random = list(~1|Study_ID,
~1|Phylogeny,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
R= list(Phylogeny = cor_tree_dry), # cor_tree_dry here
test = "t",
data = data)
}full_model_dry <- run_model_dry(dry_dat, ~ scale(Length_cooking_time_in_s)) # Model does not converge with VCV_lnRR
summary(full_model_dry)##
## Multivariate Meta-Analysis Model (k = 47; method: REML)
##
## logLik Deviance AIC BIC AICc
## -9.5927 19.1854 31.1854 42.0254 33.3959
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.0000 0.0000 1 yes Study_ID no
## sigma^2.2 0.0036 0.0602 8 no Phylogeny yes
## sigma^2.3 0.0143 0.1195 8 no Species_common no
## sigma^2.4 0.0772 0.2779 2 no PFAS_type no
## sigma^2.5 0.0495 0.2225 47 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 45) = 240.4637, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 45) = 74.1203, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -0.7777 0.2102 -3.6998 45 0.0006 -1.2011
## scale(Length_cooking_time_in_s) -0.3448 0.0400 -8.6093 45 <.0001 -0.4254
## ci.ub
## intrcpt -0.3543 ***
## scale(Length_cooking_time_in_s) -0.2641 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
save(full_model_dry, file = here("Rdata", "full_model_dry.RData")) oil_dat <- filter(dat, Cooking_Category=="oil-based")
water_dat <- filter(dat, Cooking_Category=="water-based")
dry_dat <- filter(dat, Cooking_Category=="No liquid")
oil_dat_time<-filter(oil_dat, Length_cooking_time_in_s!="NA")
water_dat_time<-filter(water_dat, Length_cooking_time_in_s!="NA")
dry_dat_time<-filter(dry_dat, Length_cooking_time_in_s!="NA")
model_oil_time<-run_model_oil(oil_dat_time, ~Length_cooking_time_in_s)
model_water_time<-run_model_water(water_dat_time, ~Length_cooking_time_in_s)
model_dry_time<-run_model_dry(dry_dat_time, ~Length_cooking_time_in_s)
pred_oil_time<-predict.rma(model_oil_time)
pred_water_time<-predict.rma(model_water_time)
pred_dry_time<-predict.rma(model_dry_time)
oil_dat_time<-mutate(oil_dat_time,
ci.lb = pred_oil_time$ci.lb, # lower bound of the confidence interval for oil
ci.ub = pred_oil_time$ci.ub, # upper bound of the confidence interval for oil
fit = pred_oil_time$pred) # regression line for oil
water_dat_time<-mutate(water_dat_time,
ci.lb = pred_water_time$ci.lb, # lower bound of the confidence interval for water
ci.ub = pred_water_time$ci.ub, # upper bound of the confidence interval for water
fit = pred_water_time$pred) # regression line for water
dry_dat_time<-mutate(dry_dat_time,
ci.lb = pred_dry_time$ci.lb, # lower bound of the confidence interval for dry
ci.ub = pred_dry_time$ci.ub, # upper bound of the confidence interval for dry
fit = pred_dry_time$pred) # regression line for dryggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=water_dat_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=water_dat_time,aes(y = fit), size = 1.5, col="dodgerblue")+
geom_ribbon(data=oil_dat_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=oil_dat_time,aes(y = fit), size = 1.5, col="goldenrod")+
geom_ribbon(data=dry_dat_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=dry_dat_time,aes(y = fit), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))##### Oil based
full_model_oil_time<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_oil_time<-predict.rma(full_model_oil_time, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time<-as.data.frame(pred_oil_time)
pred_oil_time$Length_cooking_time_in_s=pred_oil_time$X.Length_cooking_time_in_s
pred_oil_time<-left_join(oil_dat, pred_oil_time, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_water_time<-predict.rma(full_model_water_time, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time<-as.data.frame(pred_water_time)
pred_water_time$Length_cooking_time_in_s=pred_water_time$X.Length_cooking_time_in_s
pred_water_time<-left_join(water_dat, pred_water_time, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time<- run_model_dry(dry_dat, ~ Length_cooking_time_in_s)
pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")
ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_oil_time,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2)) oil_dat_vol<-filter(oil_dat, Ratio_liquid_fish!="NA")
water_dat_vol<-filter(water_dat, Ratio_liquid_fish!="NA")
model_oil_vol<-run_model_oil(oil_dat_vol, ~log(Ratio_liquid_fish))
model_water_vol<-run_model_water(water_dat_vol, ~log(Ratio_liquid_fish))
pred_oil_vol<-predict.rma(model_oil_vol)
pred_water_vol<-predict.rma(model_water_vol)
oil_dat_vol<-mutate(oil_dat_vol,
ci.lb = pred_oil_vol$ci.lb,
ci.ub = pred_oil_vol$ci.ub,
fit = pred_oil_vol$pred)
water_dat_vol<-mutate(water_dat_vol,
ci.lb = pred_water_vol$ci.lb,
ci.ub = pred_water_vol$ci.ub,
fit = pred_water_vol$pred)
oil_dat$log_Ratio_liquid_fish<-log(oil_dat$Ratio_liquid_fish)
water_dat$log_Ratio_liquid_fish<-log(water_dat$Ratio_liquid_fish)ggplot(dat,aes(x = log(Ratio_liquid_fish), y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=water_dat_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=water_dat_vol,aes(y = fit), size = 1.5, col="dodgerblue")+
geom_ribbon(data=oil_dat_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=oil_dat_vol,aes(y = fit), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF","goldenrod2", "dodgerblue3"))+
labs(x = "ln(Liquid volume to tissue ratio (mL/g))", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+
theme(text = element_text(size = 18, colour = "black", hjust = 0.5),
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))##### Oil based
full_model_oil_vol<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
log_Ratio_liquid_fish)
pred_oil_vol<-predict.rma(full_model_oil_vol, addx=TRUE, newmods=cbind(0,0,0, oil_dat$log_Ratio_liquid_fish))# Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_vol<-as.data.frame(pred_oil_vol)
pred_oil_vol<-pred_oil_vol %>% mutate(Ratio_liquid_fish=exp(X.log_Ratio_liquid_fish), Cooking_Category="oil-based", lnRR=0) # for the plot to work, we need to add a column with cooking category and a column with lnRR
##### Water based
full_model_water_vol<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
log_Ratio_liquid_fish)
pred_water_vol<-predict.rma(full_model_water_vol, addx=TRUE, newmods=cbind(0,0, water_dat$log_Ratio_liquid_fish))# Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_vol<-as.data.frame(pred_water_vol)
pred_water_vol<-pred_water_vol %>% mutate(Ratio_liquid_fish=exp(X.log_Ratio_liquid_fish), Cooking_Category="water-based", lnRR=0)
ggplot(dat,aes(x = log(Ratio_liquid_fish), y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_vol,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_vol,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF","goldenrod2", "dodgerblue3"))+
labs(x = "ln(Liquid volume to tissue sample ratio (mL/g))", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+
theme(text = element_text(size = 18, colour = "black", hjust = 0.5),
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2)) #### The line doesn't go all the way down for water-based because the highest values are not included in the full model oil_dat_PFAS<-filter(oil_dat, PFAS_carbon_chain!="NA")
water_dat_PFAS<-filter(water_dat, PFAS_carbon_chain!="NA")
dry_dat_PFAS<-filter(dry_dat, PFAS_carbon_chain!="NA")
model_oil_PFAS<-run_model_oil(oil_dat_PFAS, ~PFAS_carbon_chain)
model_water_PFAS<-run_model_water(water_dat_PFAS, ~PFAS_carbon_chain)
model_dry_PFAS<-run_model_dry(dry_dat_PFAS, ~PFAS_carbon_chain)
pred_oil_PFAS<-predict.rma(model_oil_PFAS)
pred_water_PFAS<-predict.rma(model_water_PFAS)
pred_dry_PFAS<-predict.rma(model_dry_PFAS)
oil_dat_PFAS<-mutate(oil_dat_PFAS,
ci.lb = pred_oil_PFAS$ci.lb,
ci.ub = pred_oil_PFAS$ci.ub,
fit = pred_oil_PFAS$pred)
water_dat_PFAS<-mutate(water_dat_PFAS,
ci.lb = pred_water_PFAS$ci.lb,
ci.ub = pred_water_PFAS$ci.ub,
fit = pred_water_PFAS$pred)
dry_dat_PFAS<-mutate(dry_dat_PFAS,
ci.lb = pred_dry_PFAS$ci.lb,
ci.ub = pred_dry_PFAS$ci.ub,
fit = pred_dry_PFAS$pred) For some reason the plot doesn’t want to knit, although the script works
ggplot(dat, aes(x= PFAS_carbon_chain, y=lnRR, fill=Cooking_Category))+
geom_ribbon(data=dry_dat_PFAS, aes(ymin=ci.lb, ymax=ci.ub, color=NULL), alpha=0.2)+
geom_line (data=dry_dat_PFAS, aes(y=fit), size = 1.5, col="palegreen3")+
geom_ribbon(data=water_dat_PFAS, aes(ymin=ci.lb, ymax=ci.ub, color=NULL), alpha=0.2)+
geom_line (data=water_dat_PFAS, aes(y=fit), size = 1.5, col="dodgerblue")+
geom_ribbon(data=oil_dat_PFAS, aes(ymin=ci.lb, ymax=ci.ub, color=NULL), alpha=0.2)+
geom_line (data=oil_dat_PFAS, aes(y=fit), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+
theme(text = element_text(size = 18, colour = "black", hjust = 0.5),
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))##### Oil based
full_model_oil_PFAS<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_oil_PFAS<-predict.rma(full_model_oil_PFAS, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS<-as.data.frame(pred_oil_PFAS)
pred_oil_PFAS$PFAS_carbon_chain=pred_oil_PFAS$X.PFAS_carbon_chain
pred_oil_PFAS<-left_join(oil_dat, pred_oil_PFAS, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_water_PFAS<-predict.rma(full_model_water_PFAS, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS<-as.data.frame(pred_water_PFAS)
pred_water_PFAS$PFAS_carbon_chain=pred_water_PFAS$X.PFAS_carbon_chain
pred_water_PFAS<-left_join(water_dat, pred_water_PFAS, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS<- run_model_dry(dry_dat, ~ PFAS_carbon_chain)
pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")
ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))funnel(full_model, yaxis = "seinv")funnel(full_model)egger_all <- run_model(dat, ~ - 1 + Cooking_Category +
I(sqrt(1/N_tilde)) +
scale(Publication_year) +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(egger_all)##
## Multivariate Meta-Analysis Model (k = 384; method: REML)
##
## logLik Deviance AIC BIC AICc
## -414.3540 828.7080 854.7080 905.7926 855.7135
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.0337 0.1837 7 no Study_ID no
## sigma^2.2 0.0000 0.0001 26 no Phylogeny yes
## sigma^2.3 0.1929 0.4392 26 no Species_common no
## sigma^2.4 0.1158 0.3403 17 no PFAS_type no
## sigma^2.5 0.4275 0.6538 384 no Effect_ID no
##
## Test of Moderators (coefficients 1:8):
## F(df1 = 8, df2 = 376) = 13.1782, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## Cooking_Categoryoil-based -0.6683 0.3227 -2.0709 376 0.0390
## Cooking_Categorywater-based -0.8510 0.3209 -2.6522 376 0.0083
## I(sqrt(1/N_tilde)) 0.1144 0.4919 0.2325 376 0.8163
## scale(Publication_year) 0.4151 0.0938 4.4230 376 <.0001
## scale(Temperature_in_Celsius) -0.3725 0.1027 -3.6266 376 0.0003
## scale(Length_cooking_time_in_s) -0.3088 0.0480 -6.4272 376 <.0001
## scale(PFAS_carbon_chain) 0.0809 0.0772 1.0480 376 0.2953
## scale(log(Ratio_liquid_fish)) -0.8800 0.1452 -6.0614 376 <.0001
## ci.lb ci.ub
## Cooking_Categoryoil-based -1.3028 -0.0338 *
## Cooking_Categorywater-based -1.4820 -0.2201 **
## I(sqrt(1/N_tilde)) -0.8529 1.0816
## scale(Publication_year) 0.2306 0.5996 ***
## scale(Temperature_in_Celsius) -0.5745 -0.1705 ***
## scale(Length_cooking_time_in_s) -0.4033 -0.2143 ***
## scale(PFAS_carbon_chain) -0.0709 0.2328
## scale(log(Ratio_liquid_fish)) -1.1655 -0.5945 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
funnel(egger_all, yaxis = "seinv")funnel(egger_all)#funnel(egger_all, yaxis = "seinv")
# little evidence
egger_n <- run_model(dat, ~ I(sqrt(1/N_tilde)))
summary(egger_n)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -613.4723 1226.9445 1240.9445 1270.5854 1241.1676
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5917 0.7692 10 no Study_ID no
## sigma^2.2 0.0000 0.0002 38 no Phylogeny yes
## sigma^2.3 0.2412 0.4911 39 no Species_common no
## sigma^2.4 0.0941 0.3068 18 no PFAS_type no
## sigma^2.5 0.5175 0.7194 512 no Effect_ID no
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 510) = 0.0761, p-val = 0.7828
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.2571 0.3745 -0.6864 510 0.4928 -0.9928 0.4787
## I(sqrt(1/N_tilde)) -0.1422 0.5156 -0.2758 510 0.7828 -1.1551 0.8707
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
save(egger_all, egger_n, file = here("Rdata", "egger_regressions.RData"))pub_year<-run_model(dat, ~Publication_year)
summary(pub_year)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -611.8609 1223.7218 1237.7218 1267.3627 1237.9449
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5556 0.7454 10 no Study_ID no
## sigma^2.2 0.0000 0.0003 38 no Phylogeny yes
## sigma^2.3 0.2426 0.4925 39 no Species_common no
## sigma^2.4 0.0945 0.3073 18 no PFAS_type no
## sigma^2.5 0.5166 0.7188 512 no Effect_ID no
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 510) = 1.3023, p-val = 0.2543
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -166.3406 145.4805 -1.1434 510 0.2534 -452.1555
## Publication_year 0.0823 0.0721 1.1412 510 0.2543 -0.0594
## ci.ub
## intrcpt 119.4743
## Publication_year 0.2241
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot_continuous(dat, pub_year, dat$Publication_year, "Publication year") ##
Here, we iteratively removed one study at the time and investigated how it affects the overall mean. Removing one of the study particularly modifies the estimate, but none of these models show a significant overall difference in PFAS concentration with cooking.
dat$Study_ID<-as.factor(dat$Study_ID)
dat<-as.data.frame(dat) # Only work with a dataframe
VCV_matrix<-list() # will need new VCV matrices because the sample size will be iteratively reduced
Leave1studyout<-list() # create a list that will host the results of each model
for(i in 1:length(levels(dat$Study_ID))){ # N models = N studies
VCV_matrix[[i]]<-make_VCV_matrix(dat[dat$Study_ID != levels(dat$Study_ID)[i], ], V="var_lnRR", cluster="Cohort_ID", obs="Effect_ID") # Create a new VCV matrix for each new model
Leave1studyout[[i]] <- rma.mv(yi = lnRR, V = VCV_matrix[[i]], # Same model structure as all the models we fitted
random = list(~1|Study_ID,
~1|Phylogeny,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
R= list(Phylogeny = cor_tree),
test = "t",
data = dat[dat$Study_ID != levels(dat$Study_ID)[i], ]) # Generate a new model for each new data (iterative removal of one study at a time)
}
# The output is a list so we need to summarise the coefficients of all the models performed
results.Leave1studyout<-as.data.frame(cbind(
sapply(Leave1studyout, function(x) summary(x)$beta), # extract the beta coefficient from all models
sapply(Leave1studyout, function(x) summary(x)$se), # extract the standard error from all models
sapply(Leave1studyout, function(x) summary(x)$zval), # extract the z value from all models
sapply(Leave1studyout, function(x) summary(x)$pval), # extract the p value from all models
sapply(Leave1studyout, function(x) summary(x)$ci.lb), # extract the lower confidence interval for all models
sapply(Leave1studyout, function(x) summary(x)$ci.ub))) # extract the upper confidence interval for all models
colnames(results.Leave1studyout)=c("Estimate", "SE", "zval", "pval", "ci.lb", "ci.ub") # change column names
kable(results.Leave1studyout)%>% kable_styling("striped", position="left") %>% scroll_box(width="100%", height="500px") # Table of the results from all models| Estimate | SE | zval | pval | ci.lb | ci.ub |
|---|---|---|---|---|---|
| -0.3336301 | 0.3042647 | -1.0965126 | 0.2733730 | -0.9313995 | 0.2641392 |
| -0.4032485 | 0.3068818 | -1.3140189 | 0.1894400 | -1.0061795 | 0.1996825 |
| -0.4211645 | 0.3342120 | -1.2601716 | 0.2083767 | -1.0782901 | 0.2359610 |
| 0.0107956 | 0.2629087 | 0.0410620 | 0.9672633 | -0.5057851 | 0.5273762 |
| -0.3345166 | 0.3098382 | -1.0796493 | 0.2808417 | -0.9433268 | 0.2742935 |
| -0.2480966 | 0.2979255 | -0.8327470 | 0.4053956 | -0.8334747 | 0.3372815 |
| -0.3395663 | 0.3092701 | -1.0979605 | 0.2727523 | -0.9472013 | 0.2680686 |
| -0.2258479 | 0.3056843 | -0.7388272 | 0.4605552 | -0.8272598 | 0.3755641 |
| -0.3944691 | 0.3143852 | -1.2547321 | 0.2101841 | -1.0122039 | 0.2232656 |
| -0.4847429 | 0.2852116 | -1.6995908 | 0.0898914 | -1.0452382 | 0.0757524 |
dat %>% group_by(Author_year, Study_ID) %>% summarise(mean=mean(lnRR)) # Study F005 (DelGobbo_2008) has much lower effect sizes than the others. ## # A tibble: 10 × 3
## # Groups: Author_year [10]
## Author_year Study_ID mean
## <chr> <fct> <dbl>
## 1 Alves_2017 F001 -0.0774
## 2 Barbosa_2018 F002 0.198
## 3 Bhavsar_2014 F003 0.153
## 4 DelGobbo_2008 F005 -2.00
## 5 Hu_2020 F006 -0.134
## 6 Kim_2020 F007 -0.887
## 7 Luo_2019 F008 -0.161
## 8 Sungur_2019 F010 -0.893
## 9 Taylor_2019 F011 0.208
## 10 Vassiliadou_2015 F013 0.671
Study_ID F005 (Del Gobbo et al. 2008)dat.sens<-filter(dat, Author_year!="DelGobbo_2008")
include <- row.names(cor_tree) %in% dat.sens$Phylogeny # Check which rows are present in the phylogenetic tree
cor_tree_sens <- cor_tree[include, include] # Only include the species that match the reduced data set
dat.sens<-as.data.frame(dat.sens) # convert data set into a data frame to calculate VCV matrix
VCV_lnRR.sens<-make_VCV_matrix(dat.sens, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
mod.sens<- rma.mv(lnRR, VCV_lnRR.sens,
mods=~Length_cooking_time_in_s,
random = list(~1|Study_ID,
~1|Phylogeny,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
R= list(Phylogeny = cor_tree_sens),
test = "t",
data = dat.sens)
summary(mod.sens)##
## Multivariate Meta-Analysis Model (k = 430; method: REML)
##
## logLik Deviance AIC BIC AICc
## -258.4874 516.9747 530.9747 559.3886 531.2414
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1914 0.4375 8 no Study_ID no
## sigma^2.2 0.0000 0.0000 22 no Phylogeny yes
## sigma^2.3 0.0287 0.1694 22 no Species_common no
## sigma^2.4 0.0890 0.2984 17 no PFAS_type no
## sigma^2.5 0.1312 0.3622 430 no Effect_ID no
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 428) = 118.1290, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.4827 0.1958 2.4646 428 0.0141 0.0977
## Length_cooking_time_in_s -0.0010 0.0001 -10.8687 428 <.0001 -0.0012
## ci.ub
## intrcpt 0.8676 *
## Length_cooking_time_in_s -0.0008 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
dat.time.sens<-filter(dat.sens, Length_cooking_time_in_s!="NA")
plot_continuous(dat.time.sens, mod.sens, dat.time.sens$Length_cooking_time_in_s, "Cooking time (s)") # The relationship with cooking time appears even stronger oil_dat.sens <- filter(dat.sens, Cooking_Category=="oil-based")
water_dat.sens <- filter(dat.sens, Cooking_Category=="water-based")
dry_dat.sens <- filter(dat.sens, Cooking_Category=="No liquid")
oil_dat_time.sens<-filter(oil_dat.sens, Length_cooking_time_in_s!="NA")
water_dat_time.sens<-filter(water_dat.sens, Length_cooking_time_in_s!="NA")
dry_dat_time.sens<-filter(dry_dat.sens, Length_cooking_time_in_s!="NA")
model_oil_time.sens<-run_model_oil(oil_dat_time.sens, ~Length_cooking_time_in_s)
model_water_time.sens<-run_model_water(water_dat_time.sens, ~Length_cooking_time_in_s)
model_dry_time.sens<-run_model_dry(dry_dat_time.sens, ~Length_cooking_time_in_s)
summary(model_oil_time.sens)##
## Multivariate Meta-Analysis Model (k = 263; method: REML)
##
## logLik Deviance AIC BIC AICc
## -115.3663 230.7327 244.7327 269.6843 245.1754
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.3006 0.5483 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 15 no Phylogeny yes
## sigma^2.3 0.0105 0.1023 15 no Species_common no
## sigma^2.4 0.1305 0.3612 16 no PFAS_type no
## sigma^2.5 0.0803 0.2833 263 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 261) = 2628.1210, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 261) = 134.5758, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.4161 0.2773 1.5005 261 0.1347 -0.1299
## Length_cooking_time_in_s -0.0014 0.0001 -11.6007 261 <.0001 -0.0017
## ci.ub
## intrcpt 0.9621
## Length_cooking_time_in_s -0.0012 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(model_water_time.sens)##
## Multivariate Meta-Analysis Model (k = 120; method: REML)
##
## logLik Deviance AIC BIC AICc
## -101.2717 202.5435 216.5435 235.9383 217.5617
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1173 0.3425 7 no Study_ID no
## sigma^2.2 0.0000 0.0006 17 no Phylogeny yes
## sigma^2.3 0.0202 0.1423 17 no Species_common no
## sigma^2.4 0.0844 0.2905 15 no PFAS_type no
## sigma^2.5 0.2257 0.4751 120 no Effect_ID no
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 118) = 21.5446, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.5145 0.2380 2.1613 118 0.0327 0.0431
## Length_cooking_time_in_s -0.0010 0.0002 -4.6416 118 <.0001 -0.0015
## ci.ub
## intrcpt 0.9859 *
## Length_cooking_time_in_s -0.0006 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(model_dry_time.sens)##
## Multivariate Meta-Analysis Model (k = 47; method: REML)
##
## logLik Deviance AIC BIC AICc
## -9.5927 19.1854 31.1854 42.0254 33.3959
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.0000 0.0000 1 yes Study_ID no
## sigma^2.2 0.0036 0.0602 8 no Phylogeny yes
## sigma^2.3 0.0143 0.1195 8 no Species_common no
## sigma^2.4 0.0772 0.2779 2 no PFAS_type no
## sigma^2.5 0.0495 0.2225 47 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 45) = 240.4637, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 45) = 74.1203, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.4737 0.2555 1.8535 45 0.0704 -0.0410
## Length_cooking_time_in_s -0.0014 0.0002 -8.6093 45 <.0001 -0.0017
## ci.ub
## intrcpt 0.9884 .
## Length_cooking_time_in_s -0.0011 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_time.sens<-predict.rma(model_oil_time.sens)
pred_water_time.sens<-predict.rma(model_water_time.sens)
pred_dry_time.sens<-predict.rma(model_dry_time.sens)
oil_dat_time.sens<-mutate(oil_dat_time.sens,
ci.lb = pred_oil_time.sens$ci.lb,
ci.ub = pred_oil_time.sens$ci.ub,
fit = pred_oil_time.sens$pred)
water_dat_time.sens<-mutate(water_dat_time.sens,
ci.lb = pred_water_time.sens$ci.lb,
ci.ub = pred_water_time.sens$ci.ub,
fit = pred_water_time.sens$pred)
dry_dat_time.sens<-mutate(dry_dat_time.sens,
ci.lb = pred_dry_time.sens$ci.lb,
ci.ub = pred_dry_time.sens$ci.ub,
fit = pred_dry_time.sens$pred) For some reason the plot doesn’t want to knit, although the script works
# Actual plot
ggplot(dat.sens,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=water_dat_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=water_dat_time.sens,aes(y = fit), size=1.5, col="dodgerblue")+
geom_ribbon(data=oil_dat_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=oil_dat_time.sens,aes(y = fit), size = 1.5, col="goldenrod")+
geom_ribbon(data=dry_dat_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.25) +
geom_line(data=dry_dat_time.sens,aes(y = fit), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+
theme(text = element_text(size = 18, colour = "black", hjust = 0.5),
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))##### Oil based
full_model_oil_time.sens<- run_model_oil(oil_dat.sens, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(full_model_oil_time.sens)##
## Multivariate Meta-Analysis Model (k = 257; method: REML)
##
## logLik Deviance AIC BIC AICc
## -96.5618 193.1236 213.1236 248.4179 214.0365
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.4457 0.6676 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 15 no Phylogeny yes
## sigma^2.3 0.0114 0.1067 15 no Species_common no
## sigma^2.4 0.1012 0.3181 16 no PFAS_type no
## sigma^2.5 0.0699 0.2644 257 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 252) = 1914.7433, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 252) = 37.7861, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.4609 0.3233 1.4254 252 0.1553 -0.1759
## scale(Temperature_in_Celsius) 0.0699 0.1241 0.5635 252 0.5736 -0.1744
## Length_cooking_time_in_s -0.0014 0.0001 -12.1462 252 <.0001 -0.0017
## scale(PFAS_carbon_chain) 0.1302 0.0696 1.8706 252 0.0626 -0.0069
## scale(log(Ratio_liquid_fish)) 0.1973 0.2592 0.7610 252 0.4474 -0.3133
## ci.ub
## intrcpt 1.0977
## scale(Temperature_in_Celsius) 0.3143
## Length_cooking_time_in_s -0.0012 ***
## scale(PFAS_carbon_chain) 0.2672 .
## scale(log(Ratio_liquid_fish)) 0.7078
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_time.sens<-predict.rma(full_model_oil_time.sens, addx=TRUE, newmods=cbind(0,oil_dat.sens$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time.sens<-as.data.frame(pred_oil_time.sens)
pred_oil_time.sens$Length_cooking_time_in_s=pred_oil_time.sens$X.Length_cooking_time_in_s
pred_oil_time.sens<-left_join(oil_dat.sens, pred_oil_time.sens, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time.sens<- run_model_water(water_dat.sens, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(full_model_water_time.sens)##
## Multivariate Meta-Analysis Model (k = 101; method: REML)
##
## logLik Deviance AIC BIC AICc
## -61.6117 123.2234 141.2234 164.3958 143.2923
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.2775 0.5268 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 13 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 13 no Species_common no
## sigma^2.4 0.0991 0.3148 15 no PFAS_type no
## sigma^2.5 0.1275 0.3571 101 no Effect_ID no
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 97) = 19.2941, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.1093 0.3049 0.3585 97 0.7207 -0.4958
## Length_cooking_time_in_s -0.0012 0.0002 -6.0797 97 <.0001 -0.0016
## scale(PFAS_carbon_chain) 0.1686 0.0781 2.1581 97 0.0334 0.0135
## scale(log(Ratio_liquid_fish)) -0.4409 0.1368 -3.2237 97 0.0017 -0.7124
## ci.ub
## intrcpt 0.7144
## Length_cooking_time_in_s -0.0008 ***
## scale(PFAS_carbon_chain) 0.3237 *
## scale(log(Ratio_liquid_fish)) -0.1695 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_water_time.sens<-predict.rma(full_model_water_time.sens, addx=TRUE, newmods=cbind(water_dat.sens$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time.sens<-as.data.frame(pred_water_time.sens)
pred_water_time.sens$Length_cooking_time_in_s=pred_water_time.sens$X.Length_cooking_time_in_s
pred_water_time.sens<-left_join(water_dat, pred_water_time.sens, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time.sens<- run_model_dry(dry_dat.sens, ~ Length_cooking_time_in_s)
pred_dry_time.sens<-predict.rma(full_model_dry_time.sens, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time.sens<-as.data.frame(pred_dry_time.sens)
pred_dry_time.sens$Length_cooking_time_in_s=pred_dry_time.sens$X.Length_cooking_time_in_s
pred_dry_time.sens<-left_join(dry_dat.sens, pred_dry_time.sens, by="Length_cooking_time_in_s")
ggplot(dat.sens,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time.sens,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_time.sens,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time.sens,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))dat.sens.vol<-filter(dat.sens, Ratio_liquid_fish!="NA")
include <- row.names(cor_tree) %in% dat.sens.vol$Phylogeny # Check which rows are present in the phylogenetic tree
cor_tree_sens.vol <- cor_tree[include, include] # Only include the species that match the reduced data set
VCV_lnRR.sens.vol<-make_VCV_matrix(dat.sens.vol, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
mod.sens.vol<- rma.mv(lnRR, VCV_lnRR.sens.vol,
mods=~ log(Ratio_liquid_fish),
random = list(~1|Study_ID,
~1|Phylogeny,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
R= list(Phylogeny = cor_tree_sens.vol),
test = "t",
data = dat.sens.vol)
summary(mod.sens.vol)##
## Multivariate Meta-Analysis Model (k = 398; method: REML)
##
## logLik Deviance AIC BIC AICc
## -358.6303 717.2606 731.2606 759.1305 731.5492
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.2900 0.5385 7 no Study_ID no
## sigma^2.2 0.0353 0.1880 26 no Phylogeny yes
## sigma^2.3 0.1289 0.3590 27 no Species_common no
## sigma^2.4 0.1175 0.3427 18 no PFAS_type no
## sigma^2.5 0.2648 0.5146 398 no Effect_ID no
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 396) = 0.0494, p-val = 0.8241
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.1267 0.2669 -0.4749 396 0.6351 -0.6514 0.3979
## log(Ratio_liquid_fish) -0.0069 0.0312 -0.2224 396 0.8241 -0.0683 0.0544
##
## intrcpt
## log(Ratio_liquid_fish)
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot_continuous(dat.sens.vol, mod.sens.vol, log(dat.sens.vol$Ratio_liquid_fish), "ln(Liquid volume to tissue sample ratio (mL/g))") +
scale_fill_manual(values=c("goldenrod2", "dodgerblue3"))# The relationship with cooking time appears even stronger oil_dat.sens <- filter(dat.sens, Cooking_Category=="oil-based")
water_dat.sens <- filter(dat.sens, Cooking_Category=="water-based")
oil_dat_vol.sens<-filter(oil_dat.sens, Ratio_liquid_fish!="NA")
water_dat_vol.sens<-filter(water_dat.sens, Ratio_liquid_fish!="NA")
model_oil_vol.sens<-run_model_oil(oil_dat_vol.sens, ~log(Ratio_liquid_fish))
model_water_vol.sens<-run_model_water(water_dat_vol.sens, ~log(Ratio_liquid_fish))
summary(model_oil_vol.sens)##
## Multivariate Meta-Analysis Model (k = 297; method: REML)
##
## logLik Deviance AIC BIC AICc
## -280.6371 561.2742 575.2742 601.0830 575.6645
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.4059 0.6371 6 no Study_ID no
## sigma^2.2 0.0747 0.2734 23 no Phylogeny yes
## sigma^2.3 0.0991 0.3149 24 no Species_common no
## sigma^2.4 0.0810 0.2846 17 no PFAS_type no
## sigma^2.5 0.2946 0.5428 297 no Effect_ID no
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 295) = 0.0134, p-val = 0.9080
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.0586 0.3373 -0.1738 295 0.8621 -0.7225 0.6052
## log(Ratio_liquid_fish) 0.0041 0.0351 0.1157 295 0.9080 -0.0650 0.0731
##
## intrcpt
## log(Ratio_liquid_fish)
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(model_water_vol.sens)##
## Multivariate Meta-Analysis Model (k = 101; method: REML)
##
## logLik Deviance AIC BIC AICc
## -79.9298 159.8596 173.8596 192.0254 175.0904
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.6157 0.7846 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 13 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 13 no Species_common no
## sigma^2.4 0.1167 0.3417 15 no PFAS_type no
## sigma^2.5 0.1927 0.4390 101 no Effect_ID no
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 99) = 13.6916, p-val = 0.0004
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.6143 0.4613 1.3316 99 0.1861 -0.3011 1.5297
## log(Ratio_liquid_fish) -0.5274 0.1425 -3.7002 99 0.0004 -0.8102 -0.2446
##
## intrcpt
## log(Ratio_liquid_fish) ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_vol.sens<-predict.rma(model_oil_vol.sens)
pred_water_vol.sens<-predict.rma(model_water_vol.sens)
oil_dat_vol.sens<-mutate(oil_dat_vol.sens,
ci.lb = pred_oil_vol.sens$ci.lb,
ci.ub = pred_oil_vol.sens$ci.ub,
fit = pred_oil_vol.sens$pred)
water_dat_vol.sens<-mutate(water_dat_vol.sens,
ci.lb = pred_water_vol.sens$ci.lb,
ci.ub = pred_water_vol.sens$ci.ub,
fit = pred_water_vol.sens$pred) For some reason the plot doesn’t want to knit, although the script works
# Actual plot
ggplot(dat.sens,aes(x = log(Ratio_liquid_fish), y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=water_dat_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=water_dat_vol.sens,aes(y = fit), size = 1.5, col="dodgerblue")+
geom_ribbon(data=oil_dat_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=oil_dat_vol.sens,aes(y = fit), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "ln(Liquid volume to tissue sample ratio)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+
theme(text = element_text(size = 18, colour = "black", hjust = 0.5),
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))##### Oil based
full_model_oil_vol.sens<- run_model_oil(oil_dat.sens, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
log_Ratio_liquid_fish)
summary(full_model_oil_vol.sens)##
## Multivariate Meta-Analysis Model (k = 257; method: REML)
##
## logLik Deviance AIC BIC AICc
## -96.5618 193.1236 213.1236 248.4179 214.0365
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.4457 0.6676 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 15 no Phylogeny yes
## sigma^2.3 0.0114 0.1067 15 no Species_common no
## sigma^2.4 0.1012 0.3181 16 no PFAS_type no
## sigma^2.5 0.0699 0.2644 257 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 252) = 1914.7433, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 252) = 37.7861, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.5516 0.3219 -1.7139 252 0.0878
## scale(Temperature_in_Celsius) 0.0699 0.1241 0.5635 252 0.5736
## scale(Length_cooking_time_in_s) -0.3762 0.0310 -12.1462 252 <.0001
## scale(PFAS_carbon_chain) 0.1302 0.0696 1.8706 252 0.0626
## log_Ratio_liquid_fish 0.0604 0.0794 0.7610 252 0.4474
## ci.lb ci.ub
## intrcpt -1.1855 0.0822 .
## scale(Temperature_in_Celsius) -0.1744 0.3143
## scale(Length_cooking_time_in_s) -0.4372 -0.3152 ***
## scale(PFAS_carbon_chain) -0.0069 0.2672 .
## log_Ratio_liquid_fish -0.0959 0.2168
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_vol.sens<-predict.rma(full_model_oil_vol.sens, addx=TRUE, newmods=cbind(0,0,0, oil_dat.sens$log_Ratio_liquid_fish))# Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_vol.sens<-as.data.frame(pred_oil_vol.sens)
pred_oil_vol.sens<-pred_oil_vol.sens %>% mutate(Ratio_liquid_fish=exp(X.log_Ratio_liquid_fish), Cooking_Category="oil-based", lnRR=0) # for the plot to work, we need to add a column with cooking category and a column with lnRR
##### Water based
full_model_water_vol.sens<- run_model_water(water_dat.sens, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
log_Ratio_liquid_fish)
summary(full_model_water_vol.sens)##
## Multivariate Meta-Analysis Model (k = 101; method: REML)
##
## logLik Deviance AIC BIC AICc
## -61.6117 123.2234 141.2234 164.3958 143.2923
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.2775 0.5268 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 13 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 13 no Species_common no
## sigma^2.4 0.0991 0.3148 15 no PFAS_type no
## sigma^2.5 0.1275 0.3571 101 no Effect_ID no
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 97) = 19.2941, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.2287 0.3478 0.6575 97 0.5124 -0.4617
## scale(Length_cooking_time_in_s) -0.4266 0.0702 -6.0797 97 <.0001 -0.5659
## scale(PFAS_carbon_chain) 0.1686 0.0781 2.1581 97 0.0334 0.0135
## log_Ratio_liquid_fish -0.3840 0.1191 -3.2237 97 0.0017 -0.6204
## ci.ub
## intrcpt 0.9191
## scale(Length_cooking_time_in_s) -0.2874 ***
## scale(PFAS_carbon_chain) 0.3237 *
## log_Ratio_liquid_fish -0.1476 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_water_vol.sens<-predict.rma(full_model_water_vol.sens, addx=TRUE, newmods=cbind(0,0, water_dat.sens$log_Ratio_liquid_fish))# Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_vol.sens<-as.data.frame(pred_water_vol.sens)
pred_water_vol.sens<-pred_water_vol.sens %>% mutate(Ratio_liquid_fish=exp(X.log_Ratio_liquid_fish), Cooking_Category="water-based", lnRR=0)For some reason the plot doesn’t want to knit, although the script works
ggplot(dat.sens,aes(x = log(Ratio_liquid_fish), y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_vol.sens,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_vol.sens,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF","goldenrod2", "dodgerblue3"))+
labs(x = "ln(Liquid volume to tissue sample ratio)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+
theme(text = element_text(size = 18, colour = "black", hjust = 0.5),
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2)) #### The line doesn't go all the way down (the predict function doesn't capture the biggest values)dat.sens.PFAS<-filter(dat.sens, PFAS_carbon_chain!="NA")
include <- row.names(cor_tree) %in% dat.sens.PFAS$Phylogeny # Check which rows are present in the phylogenetic tree
cor_tree_sens.PFAS <- cor_tree[include, include] # Only include the species that match the reduced data set
VCV_lnRR.sens.PFAS<-make_VCV_matrix(dat.sens.PFAS, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
mod.sens.PFAS<- rma.mv(lnRR, VCV_lnRR.sens.PFAS,
mods=~ PFAS_carbon_chain,
random = list(~1|Study_ID,
~1|Phylogeny,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
R= list(Phylogeny = cor_tree_sens.PFAS),
test = "t",
data = dat.sens.PFAS)
summary(mod.sens.PFAS)##
## Multivariate Meta-Analysis Model (k = 486; method: REML)
##
## logLik Deviance AIC BIC AICc
## -440.6231 881.2462 895.2462 924.5208 895.4815
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.2388 0.4887 9 no Study_ID no
## sigma^2.2 0.0857 0.2927 30 no Phylogeny yes
## sigma^2.3 0.1281 0.3579 31 no Species_common no
## sigma^2.4 0.0858 0.2929 18 no PFAS_type no
## sigma^2.5 0.2743 0.5237 486 no Effect_ID no
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 484) = 1.4952, p-val = 0.2220
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.2744 0.3533 -0.7769 484 0.4376 -0.9686 0.4197
## PFAS_carbon_chain 0.0319 0.0261 1.2228 484 0.2220 -0.0194 0.0833
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot_continuous(dat.sens.PFAS, mod.sens.PFAS, dat.sens.PFAS$PFAS_carbon_chain, "PFAS carbon chain length") # The relationship with cooking time appears even stronger oil_dat.sens <- filter(dat.sens, Cooking_Category=="oil-based")
water_dat.sens <- filter(dat.sens, Cooking_Category=="water-based")
dry_dat.sens <- filter(dat.sens, Cooking_Category=="No liquid")
oil_dat_PFAS.sens<-filter(oil_dat.sens, PFAS_carbon_chain!="NA")
water_dat_PFAS.sens<-filter(water_dat.sens, PFAS_carbon_chain!="NA")
dry_dat_PFAS.sens<-filter(dry_dat.sens, PFAS_carbon_chain!="NA")
model_oil_PFAS.sens<-run_model_oil(oil_dat_PFAS.sens, ~PFAS_carbon_chain)
model_water_PFAS.sens<-run_model_water(water_dat_PFAS.sens, ~PFAS_carbon_chain)
model_dry_PFAS.sens<-run_model_dry(dry_dat_PFAS.sens, ~PFAS_carbon_chain)
summary(model_oil_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 297; method: REML)
##
## logLik Deviance AIC BIC AICc
## -280.3964 560.7927 574.7927 600.6016 575.1830
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.3713 0.6094 6 no Study_ID no
## sigma^2.2 0.0836 0.2892 23 no Phylogeny yes
## sigma^2.3 0.0969 0.3114 24 no Species_common no
## sigma^2.4 0.0719 0.2682 17 no PFAS_type no
## sigma^2.5 0.2951 0.5432 297 no Effect_ID no
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 295) = 1.5686, p-val = 0.2114
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.3623 0.4127 -0.8779 295 0.3807 -1.1746 0.4499
## PFAS_carbon_chain 0.0346 0.0276 1.2524 295 0.2114 -0.0197 0.0889
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(model_water_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 120; method: REML)
##
## logLik Deviance AIC BIC AICc
## -109.3354 218.6707 232.6707 252.0655 233.6889
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.1295 0.3599 7 no Study_ID no
## sigma^2.2 0.0636 0.2521 17 no Phylogeny yes
## sigma^2.3 0.0317 0.1781 17 no Species_common no
## sigma^2.4 0.0574 0.2395 15 no PFAS_type no
## sigma^2.5 0.2664 0.5161 120 no Effect_ID no
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 118) = 2.4842, p-val = 0.1177
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.6233 0.3546 -1.7578 118 0.0814 -1.3256 0.0789 .
## PFAS_carbon_chain 0.0504 0.0320 1.5761 118 0.1177 -0.0129 0.1138
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(model_dry_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 69; method: REML)
##
## logLik Deviance AIC BIC AICc
## -66.7170 133.4339 147.4339 162.8668 149.3322
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5079 0.7127 2 no Study_ID no
## sigma^2.2 0.1854 0.4306 13 no Phylogeny yes
## sigma^2.3 0.0463 0.2152 14 no Species_common no
## sigma^2.4 0.0237 0.1538 7 no PFAS_type no
## sigma^2.5 0.3128 0.5593 69 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 67) = 4173.3255, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 67) = 5.2027, p-val = 0.0257
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -1.1811 0.8594 -1.3744 67 0.1739 -2.8965 0.5342
## PFAS_carbon_chain 0.1611 0.0706 2.2809 67 0.0257 0.0201 0.3021 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_PFAS.sens<-predict.rma(model_oil_PFAS.sens)
pred_water_PFAS.sens<-predict.rma(model_water_PFAS.sens)
pred_dry_PFAS.sens<-predict.rma(model_dry_PFAS.sens)
oil_dat_PFAS.sens<-mutate(oil_dat_PFAS.sens,
ci.lb = pred_oil_PFAS.sens$ci.lb,
ci.ub = pred_oil_PFAS.sens$ci.ub,
fit = pred_oil_PFAS.sens$pred)
water_dat_PFAS.sens<-mutate(water_dat_PFAS.sens,
ci.lb = pred_water_PFAS.sens$ci.lb,
ci.ub = pred_water_PFAS.sens$ci.ub,
fit = pred_water_PFAS.sens$pred)
dry_dat_PFAS.sens<-mutate(dry_dat_PFAS.sens,
ci.lb = pred_dry_PFAS.sens$ci.lb,
ci.ub = pred_dry_PFAS.sens$ci.ub,
fit = pred_dry_PFAS.sens$pred) For some reason the plot doesn’t want to knit, although the script works
ggplot(dat.sens,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=dry_dat_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=dry_dat_PFAS.sens,aes(y = fit), size = 1.5, col="palegreen3")+
geom_ribbon(data=oil_dat_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=oil_dat_PFAS.sens,aes(y = fit), size = 1.5, col="goldenrod")+
geom_ribbon(data=water_dat_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=water_dat_PFAS.sens,aes(y = fit), size = 1.5, col="dodgerblue")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+
theme(text = element_text(size = 18, colour = "black", hjust = 0.5),
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))##### Oil based
full_model_oil_PFAS.sens<- run_model_oil(oil_dat.sens, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
summary(full_model_oil_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 257; method: REML)
##
## logLik Deviance AIC BIC AICc
## -96.5618 193.1236 213.1236 248.4179 214.0365
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.4457 0.6676 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 15 no Phylogeny yes
## sigma^2.3 0.0114 0.1067 15 no Species_common no
## sigma^2.4 0.1012 0.3181 16 no PFAS_type no
## sigma^2.5 0.0699 0.2644 257 no Effect_ID no
##
## Test for Residual Heterogeneity:
## QE(df = 252) = 1914.7433, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 252) = 37.7861, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -1.0839 0.4098 -2.6449 252 0.0087
## scale(Temperature_in_Celsius) 0.0699 0.1241 0.5635 252 0.5736
## scale(Length_cooking_time_in_s) -0.3762 0.0310 -12.1462 252 <.0001
## PFAS_carbon_chain 0.0527 0.0282 1.8706 252 0.0626
## scale(log(Ratio_liquid_fish)) 0.1973 0.2592 0.7610 252 0.4474
## ci.lb ci.ub
## intrcpt -1.8909 -0.2768 **
## scale(Temperature_in_Celsius) -0.1744 0.3143
## scale(Length_cooking_time_in_s) -0.4372 -0.3152 ***
## PFAS_carbon_chain -0.0028 0.1082 .
## scale(log(Ratio_liquid_fish)) -0.3133 0.7078
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_PFAS.sens<-predict.rma(full_model_oil_PFAS.sens, addx=TRUE, newmods=cbind(0,0, oil_dat.sens$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS.sens<-as.data.frame(pred_oil_PFAS.sens)
pred_oil_PFAS.sens$PFAS_carbon_chain=pred_oil_PFAS.sens$X.PFAS_carbon_chain
pred_oil_PFAS.sens<-left_join(oil_dat.sens, pred_oil_PFAS.sens, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS.sens<- run_model_water(water_dat.sens, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
summary(full_model_water_PFAS.sens)##
## Multivariate Meta-Analysis Model (k = 101; method: REML)
##
## logLik Deviance AIC BIC AICc
## -61.6117 123.2234 141.2234 164.3958 143.2923
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.2775 0.5268 5 no Study_ID no
## sigma^2.2 0.0000 0.0000 13 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 13 no Species_common no
## sigma^2.4 0.0991 0.3148 15 no PFAS_type no
## sigma^2.5 0.1275 0.3571 101 no Effect_ID no
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 97) = 19.2941, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -1.3761 0.3944 -3.4891 97 0.0007 -2.1589
## scale(Length_cooking_time_in_s) -0.4266 0.0702 -6.0797 97 <.0001 -0.5659
## PFAS_carbon_chain 0.0744 0.0345 2.1581 97 0.0334 0.0060
## scale(log(Ratio_liquid_fish)) -0.4409 0.1368 -3.2237 97 0.0017 -0.7124
## ci.ub
## intrcpt -0.5933 ***
## scale(Length_cooking_time_in_s) -0.2874 ***
## PFAS_carbon_chain 0.1428 *
## scale(log(Ratio_liquid_fish)) -0.1695 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_water_PFAS.sens<-predict.rma(full_model_water_PFAS.sens, addx=TRUE, newmods=cbind(0, water_dat.sens$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS.sens<-as.data.frame(pred_water_PFAS.sens)
pred_water_PFAS.sens$PFAS_carbon_chain=pred_water_PFAS.sens$X.PFAS_carbon_chain
pred_water_PFAS.sens<-left_join(water_dat.sens, pred_water_PFAS.sens, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS.sens<- run_model_dry(dry_dat.sens, ~ PFAS_carbon_chain)
pred_dry_PFAS.sens<-predict.rma(full_model_dry_PFAS.sens, addx=TRUE)
pred_dry_PFAS.sens<-as.data.frame(pred_dry_PFAS.sens)
pred_dry_PFAS.sens$PFAS_carbon_chain=pred_dry_PFAS.sens$X.PFAS_carbon_chain
pred_dry_PFAS.sens<-left_join(dry_dat.sens, pred_dry_PFAS.sens, by="PFAS_carbon_chain")
ggplot(dat.sens,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS.sens,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS.sens,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS.sens,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))full_mod.sens<-run_model(dat.sens, ~ -1 + Cooking_Category +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
funnel(full_mod.sens, yaxis="seinv")run_model(dat, ~ -1 +
Cooking_Category +
I(sqrt(1/N_tilde)) +
scale(Publication_year) +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))##
## Multivariate Meta-Analysis Model (k = 384; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.0337 0.1837 7 no Study_ID no
## sigma^2.2 0.0000 0.0001 26 no Phylogeny yes
## sigma^2.3 0.1929 0.4392 26 no Species_common no
## sigma^2.4 0.1158 0.3403 17 no PFAS_type no
## sigma^2.5 0.4275 0.6538 384 no Effect_ID no
##
## Test of Moderators (coefficients 1:8):
## F(df1 = 8, df2 = 376) = 13.1782, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## Cooking_Categoryoil-based -0.6683 0.3227 -2.0709 376 0.0390
## Cooking_Categorywater-based -0.8510 0.3209 -2.6522 376 0.0083
## I(sqrt(1/N_tilde)) 0.1144 0.4919 0.2325 376 0.8163
## scale(Publication_year) 0.4151 0.0938 4.4230 376 <.0001
## scale(Temperature_in_Celsius) -0.3725 0.1027 -3.6266 376 0.0003
## scale(Length_cooking_time_in_s) -0.3088 0.0480 -6.4272 376 <.0001
## scale(PFAS_carbon_chain) 0.0809 0.0772 1.0480 376 0.2953
## scale(log(Ratio_liquid_fish)) -0.8800 0.1452 -6.0614 376 <.0001
## ci.lb ci.ub
## Cooking_Categoryoil-based -1.3028 -0.0338 *
## Cooking_Categorywater-based -1.4820 -0.2201 **
## I(sqrt(1/N_tilde)) -0.8529 1.0816
## scale(Publication_year) 0.2306 0.5996 ***
## scale(Temperature_in_Celsius) -0.5745 -0.1705 ***
## scale(Length_cooking_time_in_s) -0.4033 -0.2143 ***
## scale(PFAS_carbon_chain) -0.0709 0.2328
## scale(log(Ratio_liquid_fish)) -1.1655 -0.5945 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
full_model_time<- run_model(dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_full_model_time<-predict.rma(full_model_time, addx=TRUE, newmods=cbind(0,dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_time<-as.data.frame(pred_full_model_time)
pred_full_model_time$Length_cooking_time_in_s=pred_full_model_time$X.Length_cooking_time_in_s
pred_full_model_time<-left_join(dat, pred_full_model_time, by="Length_cooking_time_in_s")
uni_model_time<- run_model(dat, ~ Length_cooking_time_in_s)
pred_uni_model_time<-predict.rma(uni_model_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_time<-as.data.frame(pred_uni_model_time)
pred_uni_model_time$Length_cooking_time_in_s=pred_uni_model_time$X.Length_cooking_time_in_s
pred_uni_model_time<-left_join(dat, pred_uni_model_time, by="Length_cooking_time_in_s")
p_time<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_time, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_time,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_time, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_time,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))full_model_vol<- run_model(dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
log_Ratio_liquid_fish)
pred_full_model_vol<-predict.rma(full_model_vol, addx=TRUE, newmods=cbind(0,0, 0, dat$log_Ratio_liquid_fish))
pred_full_model_vol<-as.data.frame(pred_full_model_vol)
pred_full_model_vol$log_Ratio_liquid_fish=pred_full_model_vol$X.log_Ratio_liquid_fish
pred_full_model_vol<- pred_full_model_vol %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), lnRR = 0)
uni_model_vol<- run_model(dat, ~ log_Ratio_liquid_fish)
pred_uni_model_vol<-predict.rma(uni_model_vol, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_vol<-as.data.frame(pred_uni_model_vol)
pred_uni_model_vol$log_Ratio_liquid_fish=pred_uni_model_vol$X.log_Ratio_liquid_fish
pred_uni_model_vol<- pred_uni_model_vol %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), lnRR = 0)
p_vol<-ggplot(dat,aes(x = log_Ratio_liquid_fish, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_vol, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_vol,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_vol, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_vol,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "ln (Liquid volume to tissue sample ratio (mL/g))", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))full_model_temp<- run_model(dat, ~ Temperature_in_Celsius +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_full_model_temp<-predict.rma(full_model_temp, addx=TRUE, newmods=cbind(dat$Temperature_in_Celsius,0, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_temp<-as.data.frame(pred_full_model_temp)
pred_full_model_temp$Temperature_in_Celsius=pred_full_model_temp$X.Temperature_in_Celsius
pred_full_model_temp<-left_join(dat, pred_full_model_temp, by="Temperature_in_Celsius")
uni_model_temp<- run_model(dat, ~ Temperature_in_Celsius)
pred_uni_model_temp<-predict.rma(uni_model_temp, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_temp<-as.data.frame(pred_uni_model_temp)
pred_uni_model_temp$Temperature_in_Celsius=pred_uni_model_temp$X.Temperature_in_Celsius
pred_uni_model_temp<-left_join(dat, pred_uni_model_temp, by="Temperature_in_Celsius")
p_temp<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_temp, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_temp,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_temp, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_temp,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))full_model_PFAS<- run_model(dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_full_model_PFAS<-predict.rma(full_model_PFAS, addx=TRUE, newmods=cbind(0, 0, dat$PFAS_carbon_chain, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_PFAS<-as.data.frame(pred_full_model_PFAS)
pred_full_model_PFAS$PFAS_carbon_chain=pred_full_model_PFAS$X.PFAS_carbon_chain
pred_full_model_PFAS<-left_join(dat, pred_full_model_PFAS, by="PFAS_carbon_chain")
uni_model_PFAS<- run_model(dat, ~ PFAS_carbon_chain)
pred_uni_model_PFAS<-predict.rma(uni_model_PFAS, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_PFAS<-as.data.frame(pred_uni_model_PFAS)
pred_uni_model_PFAS$PFAS_carbon_chain=pred_uni_model_PFAS$X.PFAS_carbon_chain
pred_uni_model_PFAS<-left_join(dat, pred_uni_model_PFAS, by="PFAS_carbon_chain")
p_PFAS<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_PFAS, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_PFAS,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_PFAS, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_PFAS,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))(p_time+p_vol)/(p_temp+p_PFAS) + plot_annotation(tag_levels=c('A', 'B', 'C', 'D'))ggsave("fig/Fig_2.png", width=15, height=12, dpi=1200)my_orchard<-function (object, mod = "Int", xlab, N = "none",
alpha = 0.5, angle = 90, cb = FALSE, k = TRUE, transfm = c("none",
"tanh"), condition.lab = "Condition")
{
transfm <- match.arg(transfm)
if (any(class(object) %in% c("rma.mv", "rma"))) {
if (mod != "Int") {
object <- mod_results(object, mod)
}
else {
object <- mod_results(object, mod = "Int")
}
}
mod_table <- object$mod_table
data <- object$data
data$moderator <- factor(data$moderator, levels = mod_table$name,
labels = mod_table$name)
data$scale <- (1/sqrt(data[, "vi"]))
legend <- "Precision (1/SE)"
if (any(N != "none")) {
data$scale <- N
legend <- "Sample Size (N)"
}
if (transfm == "tanh") {
cols <- sapply(mod_table, is.numeric)
mod_table[, cols] <- Zr_to_r(mod_table[, cols])
data$yi <- Zr_to_r(data$yi)
label <- xlab
}
else {
label <- xlab
}
mod_table$K <- as.vector(by(data, data[, "moderator"],
function(x) length(x[, "yi"])))
group_no <- length(unique(mod_table[, "name"]))
cbpl <- c("#55C667FF", "goldenrod2", "dodgerblue3") # change colors
if (names(mod_table)[2] == "condition") {
condition_no <- length(unique(mod_table[, "condition"]))
plot <- ggplot2::ggplot() + ggbeeswarm::geom_quasirandom(data = data,
ggplot2::aes(y = yi, x = moderator, size = scale,
color = moderator), alpha = alpha) + ggplot2::geom_hline(yintercept = 0,
linetype = 2, colour = "black", alpha = alpha) +
ggplot2::geom_linerange(data = mod_table, ggplot2::aes(x = name,
ymin = lowerPR, ymax = upperPR), size = 0.75, # change size confidence intervals and swap CL with PR. Added whiskers
position = ggplot2::position_dodge2(width = 0.3)) +
ggplot2::geom_pointrange(data = mod_table, ggplot2::aes(y = estimate,
x = name, ymin = lowerCL, ymax = upperCL, shape = as.factor(condition), # swap CL with PR
fill = name), size = 1.6, stroke=2.2, width= 1.3, position = ggplot2::position_dodge2(width = 0.3)) + # change size point and prediction intervals
ggplot2::scale_shape_manual(values = 20 + (1:condition_no)) +
ggplot2::coord_flip() + ggplot2::theme_bw() + ggplot2::guides(fill = "none",
colour = "none") + ggplot2::theme(legend.position = c(0,
1), legend.justification = c(0, 1)) + ggplot2::theme(legend.title = ggplot2::element_text(size = 9)) +
ggplot2::theme(legend.direction = "horizontal") +
ggplot2::theme(legend.background = ggplot2::element_blank()) +
ggplot2::labs(y = label, x = "", size = legend) +
ggplot2::labs(shape = condition.lab) + ggplot2::theme(axis.text.y = ggplot2::element_text(size = 10,
colour = "black", hjust = 0.5, angle = angle))
plot <- plot + ggplot2::annotate("text", y = (max(data$yi) +
(max(data$yi) * 0.1)), x = (seq(1, group_no, 1) +
0.3), label = paste("italic(k)==", mod_table$K[1:group_no]),
parse = TRUE, hjust = "right", size = 3.5)
}
else {
plot <- ggplot2::ggplot(data = mod_table, ggplot2::aes(x = estimate,
y = name)) + ggbeeswarm::geom_quasirandom(data = data,
ggplot2::aes(x = yi, y = moderator, size = scale,
colour = moderator), groupOnX = FALSE, alpha = alpha) +
ggplot2::geom_errorbarh(ggplot2::aes(xmin = lowerPR,
xmax = upperPR), height = 0, show.legend = FALSE, # change error barrs
size = 0.75, alpha = 0.5) + ggplot2::geom_errorbarh(ggplot2::aes(xmin = lowerCL,
xmax = upperCL), height = 0.1, show.legend = FALSE,
size = 1.75) + ggplot2::geom_vline(xintercept = 0,
linetype = 2, colour = "black", alpha = alpha) +
ggplot2::geom_point(ggplot2::aes(fill = name), size = 8, # change point size
shape = 21) + ggplot2::theme_bw() + ggplot2::guides(fill = "none",
colour = "none") + ggplot2::theme(legend.position = c(1,
0), legend.justification = c(1, 0)) + ggplot2::theme(legend.title = ggplot2::element_text(size = 9)) +
ggplot2::theme(legend.direction = "horizontal") +
ggplot2::theme(legend.background = ggplot2::element_blank()) +
ggplot2::labs(x = label, y = "", size = legend) +
ggplot2::theme(axis.text.y = ggplot2::element_text(size = 10,
colour = "black", hjust = 0.5, angle = angle))
if (k == TRUE) {
plot <- plot + ggplot2::annotate("text", x = (max(data$yi) +
(max(data$yi) * 0.1)), y = (seq(1, group_no,
1) + 0.3), label = paste("italic(k)==",
mod_table$K), parse = TRUE, hjust = "right",
size = 3.5)
}
}
if (cb == TRUE) {
plot <- plot + ggplot2::scale_fill_manual(values = cbpl) +
ggplot2::scale_colour_manual(values = cbpl)
}
return(plot)
}full_model_org_units <- run_model(dat, ~ - 1 +
Cooking_Category +
Temperature_in_Celsius +
Length_cooking_time_in_s +
PFAS_carbon_chain +
log_Ratio_liquid_fish)
# full model without the "No liquid" data for figure 3B.
full_model_org_units_oil_water <- run_model(dat_oil_water, ~ - 1 +
Cooking_Category +
Temperature_in_Celsius +
Length_cooking_time_in_s +
PFAS_carbon_chain +
log_Ratio_liquid_fish)Estimates at cooking times of 2, 10 and 25 min
time_mm <-marginal_means(full_model_org_units, data = dat, mod = "1", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm<-my_orchard(time_mm, xlab = "lnRR", condition.lab = "Cooking time (sec)", alpha=0.3)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values="gray75")+
scale_colour_manual(values = "gray60")+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10),
legend.position = c(0,0.13))+
guides(size=F)Estimates at 0.1 mL/g of tissue, 10 mL/g of tissue or 45 mL/g of tissue
volume_mm <-marginal_means(full_model_org_units_oil_water, data = dat_oil_water, mod = "1", at = list(log_Ratio_liquid_fish= c(-2.3, 2.3, 3.8)), by = "log_Ratio_liquid_fish")
p_volume_mm<-my_orchard(volume_mm, xlab = "lnRR", condition.lab = "ln (Liquid to sample ratio)", alpha=0.3)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values="gray75")+
scale_colour_manual(values = "gray60")+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10),
legend.position = c(0,0.13))+
guides(size=F)Estimates at cooking times of 2, 10 and 25 min
time_mm_cat <- marginal_means(full_model_org_units, data = dat, mod = "Cooking_Category", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm_cat<-my_orchard(time_mm_cat, xlab = "lnRR", condition.lab = "Cooking time (sec)", alpha=0.3)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values=c("goldenrod2", "dodgerblue3"))+
scale_colour_manual(values = c("goldenrod2", "dodgerblue3"))+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10),
legend.position = c(0,0.12))((p_time_mm/p_volume_mm)|p_time_mm_cat) + plot_annotation(tag_levels=c("A", "B", "C"))ggsave("fig/Fig_3.png", width=14, height=10, dpi=1200)##### Oil based
full_model_oil_time<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_oil_time<-predict.rma(full_model_oil_time, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time<-as.data.frame(pred_oil_time)
pred_oil_time$Length_cooking_time_in_s=pred_oil_time$X.Length_cooking_time_in_s
pred_oil_time<-left_join(oil_dat, pred_oil_time, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_water_time<-predict.rma(full_model_water_time, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time<-as.data.frame(pred_water_time)
pred_water_time$Length_cooking_time_in_s=pred_water_time$X.Length_cooking_time_in_s
pred_water_time<-left_join(water_dat, pred_water_time, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time<- run_model_dry(dry_dat, ~ Length_cooking_time_in_s)
pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")
p_4A<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_oil_time,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
guides(fill=F)##### Oil based
full_model_oil_vol<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
log_Ratio_liquid_fish)
pred_oil_vol<-predict.rma(full_model_oil_vol, addx=TRUE, newmods=cbind(0,0,0, oil_dat$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_vol<-as.data.frame(pred_oil_vol)
pred_oil_vol<-pred_oil_vol %>% mutate(Ratio_liquid_fish=exp(X.log_Ratio_liquid_fish), Cooking_Category="oil-based", lnRR=0) # for the plot to work, we need to add a column with cooking category and a column with lnRR
##### Water based
full_model_water_vol<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
log_Ratio_liquid_fish)
pred_water_vol<-predict.rma(full_model_water_vol, addx=TRUE, newmods=cbind(0,0, water_dat$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_vol<-as.data.frame(pred_water_vol)
pred_water_vol<-pred_water_vol %>% mutate(Ratio_liquid_fish=exp(X.log_Ratio_liquid_fish), Cooking_Category="water-based", lnRR=0)
p_4B<- ggplot(dat,aes(x = log(Ratio_liquid_fish), y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_vol,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_vol,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF","goldenrod2", "dodgerblue3"))+
labs(x = "ln(Liquid volume to tissue sample ratio (mL/g)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+
theme(text = element_text(size = 18, colour = "black", hjust = 0.5),
legend.text=element_text(size=14),
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2)) #### The line doesn't go all the way down for water-based because the highest values are not included in the full modelfull_model_oil_temp<- run_model_oil(oil_dat, ~ Temperature_in_Celsius +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_oil_temp<-predict.rma(full_model_oil_temp, addx=TRUE, newmods=cbind(oil_dat$Temperature_in_Celsius,0, 0,0))
pred_oil_temp<-as.data.frame(pred_oil_temp)
pred_oil_temp$Temperature_in_Celsius=pred_oil_temp$X.Temperature_in_Celsius
pred_oil_temp<-left_join(oil_dat, pred_oil_temp, by="Temperature_in_Celsius")
p_4C<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_oil_temp, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_temp,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
guides(size=F)##### Oil based
full_model_oil_PFAS<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_oil_PFAS<-predict.rma(full_model_oil_PFAS, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS<-as.data.frame(pred_oil_PFAS)
pred_oil_PFAS$PFAS_carbon_chain=pred_oil_PFAS$X.PFAS_carbon_chain
pred_oil_PFAS<-left_join(oil_dat, pred_oil_PFAS, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_water_PFAS<-predict.rma(full_model_water_PFAS, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS<-as.data.frame(pred_water_PFAS)
pred_water_PFAS$PFAS_carbon_chain=pred_water_PFAS$X.PFAS_carbon_chain
pred_water_PFAS<-left_join(water_dat, pred_water_PFAS, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS<- run_model_dry(dry_dat, ~ PFAS_carbon_chain)
pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")
p_4D<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))(p_4A+p_4B)/(p_4C+p_4D)+ plot_annotation(tag_levels=c("A", "B", "C", "D"))ggsave("fig/Fig_4.png", width=15, height=12, dpi=1200)dat$Study_ID<- as.factor(dat$Study_ID)
# funnel(full_model,
# yaxis="seinv", # Inverse of standard error (precision) as the y axis
# level = c(90, 95, 99), # levels of statistical significance highlighted
# shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
# legend = TRUE, # display legend
# ylab="Precision (1/SE)",
# cex.lab=1.75,
# digits=1,
# cex=2,
# pch=21,
# col=dat$Study_ID)
pdf(NULL)
dev.control(displaylist="enable")
par(mar=c(4,6,0.1,0))
plot_f <- funnel(full_model,
yaxis="seinv", # Inverse of standard error (precision) as the y axis
level = c(90, 95, 99), # levels of statistical significance highlighted
shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
legend = TRUE, # display legend
ylab="Precision (1/SE)",
cex.lab=1.75,
digits=1,
ylim=c(0.82,0.94),
xlim=c(-6, 6),
cex=2,
pch=21,
col=dat$Study_ID)p_5A <- recordPlot(plot_f)
invisible(dev.off())full_model_egger <- run_model(dat, ~ - 1 +
I(sqrt(1/N_tilde)) +
scale(Publication_year) +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish))) # Model to get predictions
pred_egger<-predict.rma(full_model_egger, addx=TRUE, newmods=cbind(sqrt(1/dat$N_tilde),0,0,0 ,0, 0))
pred_egger<-as.data.frame(pred_egger)
pred_egger$SE_eff_N=pred_egger$X.I.sqrt.1.N_tilde..
pred_egger<- pred_egger %>% mutate(N_tilde = ((1/X.I.sqrt.1.N_tilde..)^2), lnRR = 0)
p_5B<-ggplot(dat,aes(x = sqrt(1/N_tilde), y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_egger, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_egger,aes(y = pred), size = 1.5, color="orangered2")+
labs(x = "Standard error", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
xlim(0.18,1)full_model_pub <- run_model(dat, ~ - 1 +
scale(I(sqrt(1/N_tilde))) +
Publication_year +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish))) # Model to get predictions
pred_pub<-predict.rma(full_model_pub, addx=TRUE, newmods=cbind(0,dat$Publication_year,0,0 ,0, 0))
pred_pub<-as.data.frame(pred_pub)
pred_pub$Publication_year=pred_pub$X.Publication_year
pred_pub<-left_join(dat, pred_pub, by="Publication_year")
p_5C<-ggplot(dat,aes(x = Publication_year, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_pub, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_pub,aes(y = pred), size = 1.5, color="orangered2")+
labs(x = "Publication year", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2)) +
scale_x_continuous(breaks=c(2008, 2010, 2012, 2014, 2016, 2018 ,2020))(ggdraw(p_5A) + ggdraw(p_5B) + ggdraw(p_5C) + plot_annotation(tag_levels = 'A'))ggsave(here("fig/Fig_5BC.png"), width=18, height=7, dpi=1200)sessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] cowplot_1.1.1 GGally_2.1.2 kableExtra_1.3.4
## [4] emmeans_1.6.3-9900006 patchwork_1.1.1 clubSandwich_0.5.3
## [7] ape_5.5 orchaRd_0.0.0.9000 metaAidR_0.0.0.9000
## [10] metafor_3.0-2 Matrix_1.3-3 here_1.0.1
## [13] googlesheets4_1.0.0 forcats_0.5.1 stringr_1.4.0
## [16] dplyr_1.0.7 purrr_0.3.4 readr_2.0.1
## [19] tidyr_1.1.3 tibble_3.1.4 ggplot2_3.3.5
## [22] tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] TH.data_1.0-10 googledrive_2.0.0 ggbeeswarm_0.6.0 colorspace_2.0-2
## [5] ellipsis_0.3.2 rprojroot_2.0.2 estimability_1.3 fs_1.5.0
## [9] rstudioapi_0.13 farver_2.1.0 fansi_0.5.0 mvtnorm_1.1-2
## [13] lubridate_1.7.10 mathjaxr_1.4-0 xml2_1.3.2 codetools_0.2-18
## [17] splines_4.1.0 knitr_1.34 jsonlite_1.7.2 broom_0.7.9
## [21] dbplyr_2.1.1 compiler_4.1.0 httr_1.4.2 backports_1.2.1
## [25] assertthat_0.2.1 fastmap_1.1.0 gargle_1.2.0 cli_3.0.1
## [29] htmltools_0.5.2 tools_4.1.0 coda_0.19-4 gtable_0.3.0
## [33] glue_1.4.2 Rcpp_1.0.7 cellranger_1.1.0 jquerylib_0.1.4
## [37] vctrs_0.3.8 svglite_2.0.0 nlme_3.1-152 xfun_0.26
## [41] rvest_1.0.1 lifecycle_1.0.0 MASS_7.3-54 zoo_1.8-9
## [45] scales_1.1.1 hms_1.1.0 parallel_4.1.0 sandwich_3.0-1
## [49] RColorBrewer_1.1-2 yaml_2.2.1 sass_0.4.0 reshape_0.8.8
## [53] stringi_1.7.4 highr_0.9 rlang_0.4.11 pkgconfig_2.0.3
## [57] systemfonts_1.0.2 evaluate_0.14 lattice_0.20-44 labeling_0.4.2
## [61] tidyselect_1.1.1 plyr_1.8.6 magrittr_2.0.1 bookdown_0.22
## [65] R6_2.5.1 generics_0.1.0 multcomp_1.4-17 DBI_1.1.1
## [69] pillar_1.6.2 haven_2.4.3 withr_2.4.2 survival_3.2-11
## [73] modelr_0.1.8 crayon_1.4.1 utf8_1.2.2 tzdb_0.1.2
## [77] rmarkdown_2.11 grid_4.1.0 readxl_1.3.1 rmdformats_1.0.2
## [81] reprex_2.0.1 digest_0.6.27 webshot_0.5.2 xtable_1.8-4
## [85] gridGraphics_0.5-1 munsell_0.5.0 beeswarm_0.4.0 viridisLite_0.4.0
## [89] vipor_0.4.5 bslib_0.3.0